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1

Neural network construction via back-propagation  

SciTech Connect (OSTI)

A method is presented that combines back-propagation with multi-layer neural network construction. Back-propagation is used not only to adjust the weights but also the signal functions. Going from one network to an equivalent one that has additional linear units, the non-linearity of these units and thus their effective presence is then introduced via back-propagation (weight-splitting). The back-propagated error causes the network to include new units in order to minimize the error function. We also show how this formalism allows to escape local minima.

Burwick, T.T.

1994-06-01T23:59:59.000Z

2

Artificial Bee Colony Training of Neural Networks: Comparison with Back-Propagation  

E-Print Network [OSTI]

Artificial Bee Colony Training of Neural Networks: Comparison with Back-Propagation John A: Artificial Bee Colony, Neural Networks, Learning, Evolution. A copy-edited version of this paper has been for optimization that has previously been applied to the training of neural networks. This paper examines more

Bullinaria, John

3

Automatic thesaurus construction for spam filtering using revised back propagation neural network  

Science Journals Connector (OSTI)

Email has become one of the fastest and most economical forms of communication. Email is also one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. This paper proposes a new spam filtering system using revised back propagation (RBP) neural network and automatic thesaurus construction. The conventional back propagation (BP) neural network has slow learning speed and is prone to trap into a local minimum, so it will lead to poor performance and efficiency. The authors present in this paper the RBP neural network to overcome the limitations of the conventional BP neural network. A well constructed thesaurus has been recognized as a valuable tool in the effective operation of text classification, it can also overcome the problems in keyword-based spam filters which ignore the relationship between words. The authors conduct the experiments on Ling-Spam corpus. Experimental results show that the proposed spam filtering system is able to achieve higher performance, especially for the combination of RBP neural network and automatic thesaurus construction.

Hao Xu; Bo Yu

2010-01-01T23:59:59.000Z

4

Back propagation artificial neural network (BPANN) based performance analysis of diesel engine using biodiesel  

Science Journals Connector (OSTI)

This work deals with the implementation of back propagation based artificial neural network (BPANN) model for prediction of the different diesel engine parameters i.e. mean effective pressure mechanical efficiency fuel consumption air-fuel ratio and torque to overcome the difficulties of practical experimentation and minimization of time and cost in this endeavor. The parameters provided as input to this model were engine speed load and different biodiesel and diesel fuel blends. The model has been trained with approximately 85% of the test results and the rest were kept for prediction. The number of hidden layers and the number of neuron in each layer were varied to achieve the best predicting model. Once the model was ready it had been used to predict the remaining data where the mean square error was as low as 6.51 × 10 ? 4 and had a very low total R value. This work hereby shows that the BPANN based model can predict the performance of diesel engine fed with biodiesel and diesel fuel blends.

Sumita Deb Barma; Biplab Das; Asis Giri; S. Majumder; P. K. Bose

2011-01-01T23:59:59.000Z

5

Prediction and Experimental Verification of CO2 Adsorption on Ni/DOBDC Using a Genetic Algorithm–Back-Propagation Neural Network Model  

Science Journals Connector (OSTI)

This is not a failure of the theoretical models but a failure of their application because the correlations from the model cannot cover a wide range of parameters. ... CO2 (as the adsorbate gas) was fed to the sample cell to achieve the setting pressure. ... The 3 layer's feed forward neural network with back propagation algorithm in MATLAB environment was used for estn. of removal efficiencies of phenol and resorcinol in bi-solute water environment based on 29 sets of lab. ...

Zhi Guo Qu; Hui Wang; Wen Zhang; Liang Zhou; Ying Xin Chang

2014-07-10T23:59:59.000Z

6

A neural network approach for image reconstruction in electron magnetic resonance tomography  

Science Journals Connector (OSTI)

An object-oriented, artificial neural network (ANN) based, application system for reconstruction of two-dimensional spatial images in electron magnetic resonance (EMR) tomography is presented. The standard back propagation algorithm is utilized to train ... Keywords: Artificial neural networks, Back propagation, Electron magnetic resonance tomography, Filtered back projection, Image reconstruction, Multiplicative algebraic reconstruction technique

D. Christopher Durairaj; Murali C. Krishna; Ramachandran Murugesan

2007-10-01T23:59:59.000Z

7

Detection of femoral artery occlusion from spectral density of Doppler signals using the artificial neural network  

Science Journals Connector (OSTI)

This research is concentrated on the diagnosis of occlusion disease through the analysis of femoral artery Doppler signals with the help of Artificial Neural Network (ANN). Doppler femoral artery signals belong to occlusion patient and healthy subjects ... Keywords: Artificial neural network, Autoregressive, Back propagation algorithm, Femoral artery occlusion, Welch

Sadik Kara; Semra Kemalo?lu; Ay?egül Güven

2005-11-01T23:59:59.000Z

8

Artificial Neural Network Modification of Simulation-Based Fitting:? Application to a Protein?Lipid System  

Science Journals Connector (OSTI)

Artificial Neural Network Modification of Simulation-Based Fitting:? Application to a Protein?Lipid System ... In our research the optimal number of neurons was estimated using the exhaustive search method. ... The application of feed forward back propagation artificial neural networks with one hidden layer (ANN) to perform the equiv. of multiple linear regression (MLR) has been examd. ...

Petr V. Nazarov; Vladimir V. Apanasovich; Vladimir M. Lutkovski; Mikalai M. Yatskou; Rob B. M. Koehorst; Marcus A. Hemminga

2004-01-17T23:59:59.000Z

9

Use of Artificial Neural Network for the Construction of Lorenz Curve  

Science Journals Connector (OSTI)

Lorenz curve and Gini index are the most widely used measures of income inequality in Economics. Artificial Neural Networks (ANN) are mathematical models that learn complex relationships in data. ANN is widely used for prediction and classification tasks ... Keywords: Artificial Neural Network, Back Propagation Algorithm, Gini Index, Lorenz Curve, Multi Layer Perceptron

Sudesh Pundir, Ganesan R.

2014-01-01T23:59:59.000Z

10

Neural Networks  

SciTech Connect (OSTI)

Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing information [2]. Each one of these cells acts as a simple processor. When individual cells interact with one another, the complex abilities of the brain are made possible. In neural networks, the input or data are processed by a propagation function that adds up the values of all the incoming data. The ending value is then compared with a threshold or specific value. The resulting value must exceed the activation function value in order to become output. The activation function is a mathematical function that a neuron uses to produce an output referring to its input value. [8] Figure 1 depicts this process. Neural networks usually have three components an input, a hidden, and an output. These layers create the end result of the neural network. A real world example is a child associating the word dog with a picture. The child says dog and simultaneously looks a picture of a dog. The input is the spoken word ''dog'', the hidden is the brain processing, and the output will be the category of the word dog based on the picture. This illustration describes how a neural network functions.

Smith, Patrick I.

2003-09-23T23:59:59.000Z

11

Artificial Neural Network  

Science Journals Connector (OSTI)

An artificial neural network (with acronym ANN), usually addressed as neural network (with acronym NN), is a model ... the structure and/or functional aspects of biological neural networks or human brain. A neural

Dr. Gómez González Daniel

2013-01-01T23:59:59.000Z

12

Determination of lithology from well logs using a neural network  

SciTech Connect (OSTI)

The authors have developed a computer program to automatically determine lithologies from well logs using a back-propagation neural network. Unlike a conventional serial computer, a neural network is a computational system composed of nodes (sometimes called neurons, neurodes, or units) and the connections between these nodes. Neural computing attempts to emulate the functions of the mammalian brain, thus mimicking thought processes. The neural network approach differs from previous pattern recognition methods in its ability to learn from examples. Unlike conventional statistical methods, this new approach does not require sophisticated mathematics and a large amount of statistical data. This paper discusses the application of neural networks to a pattern recognition problem in geology: the determination of lithology from well logs. The neural network determined the lithologies (limestone, dolomite, sandstone, shale, sandy and dolomitic limestones, sandy dolomite, and shale sandstone) from selected well logs in a fraction of the time required by an experienced human log analyst.

Rogers, S.J.; Fang, J.H. (Univ. of Alabama, Tuscaloosa (United States)); Karr, C.L.; Stanley, D.A. (Bureau of Mines, Tuscaloosa, AL (United States))

1992-05-01T23:59:59.000Z

13

Hybrid artificial neural network  

Science Journals Connector (OSTI)

Artificial neural networks (ANNs) or simply neural networks (NNs) are now a consolidated technique ... , and simulate the behavior of the biological neural network in a human brain. For that purpose ... use a sta...

Nadia Nedjah; Ajith Abraham; Luiza M. Mourelle

2007-05-01T23:59:59.000Z

14

RDP Neural Network RDP Neural Network Construction Principle  

E-Print Network [OSTI]

for building RDP Neural Networks Geometrical Approaches for Artificial Neural Networks David Elizondo Centre Elizondo Geometrical Approaches for Artificial Neural Networks #12;RDP Neural Network RDP Neural Network David Elizondo Geometrical Approaches for Artificial Neural Networks #12;RDP Neural Network RDP Neural

Gorban, Alexander N.

15

Performance of Neural Networks Methods In Intrusion Detection  

SciTech Connect (OSTI)

By accurately profiling the users via their unique attributes, it is possible to view the intrusion detection problem as a classification of authorized users and intruders. This paper demonstrates that artificial neural network (ANN) techniques can be used to solve this classification problem. Furthermore, the paper compares the performance of three neural networks methods in classifying authorized users and intruders using synthetically generated data. The three methods are the gradient descent back propagation (BP) with momentum, the conjugate gradient BP, and the quasi-Newton BP.

Dao, V N; Vemuri, R

2001-07-09T23:59:59.000Z

16

Seismic active control by neural networks.  

SciTech Connect (OSTI)

A study on the application of artificial neural networks (ANNs) to activate structural control under seismic loads is carried out. The structure considered is a single-degree-of-freedom (SDF) system with an active bracing device. The control force is computed by a trained neural network. The feed-forward neural network architecture and an adaptive back-propagation training algorithm is used in the study. The neural net is trained to reproduce the function that represents the response-excitation relationship of the SDF system under seismic loads. The input-output training patterns are generated randomly. In the back-propagation training algorithm, the learning rate is determined by ensuring the decrease of the error function at each epoch. The computer program implemented is validated by solving the classification of the XOR problem. Then, the trained ANN is used to compute the control force according to the control strategy. If the control force exceeds the actuator's capacity limit, it is set equal to that limit. The concept of the control strategy employed herein is to apply the control force at every time step to cancel the system velocity induced at the preceding time step so that the gradual rhythmic buildup of the response is destroyed. The ground motions considered in the numerical example are the 1940 El Centro earthquake and the 1979 Imperial Valley earthquake in California. The system responses with and without the control are calculated and compared. The feasibility and potential of applying ANNs to seismic active control is asserted by the promising results obtained from the numerical examples studied.

Tang, Y.

1998-01-01T23:59:59.000Z

17

Artificial Neural Networks Single Layer Networks Multi Layer Networks Generalization Artificial Neural Networks  

E-Print Network [OSTI]

Artificial Neural Networks Single Layer Networks Multi Layer Networks Generalization Artificial Neural Networks #12;Artificial Neural Networks Single Layer Networks Multi Layer Networks Generalization 1 Artificial Neural Networks Properties Applications Classical Examples Biological Background 2

Kjellström, Hedvig

18

Artificial Neural Networks Single Layer Networks Multi Layer Networks Generalization Artificial Neural Networks  

E-Print Network [OSTI]

Artificial Neural Networks Single Layer Networks Multi Layer Networks Generalization Artificial Neural Networks Artificial Neural Networks Single Layer Networks Multi Layer Networks Generalization 1 Artificial Neural Networks Properties Applications Classical Examples Biological Background 2 Single Layer

Kjellström, Hedvig

19

Morphological neural networks  

SciTech Connect (OSTI)

The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

1996-12-31T23:59:59.000Z

20

Pruning Neural Networks with Distribution Estimation Algorithms  

SciTech Connect (OSTI)

This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than the original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.

Cantu-Paz, E

2003-01-15T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

Neural Networks and Radial Basis Functions 1. Neural network theory  

E-Print Network [OSTI]

Neural Networks and Radial Basis Functions 1. Neural network theory 1. Since artificial computational solutions to problems in intelligence 2. Neural network theory has held that promise. Existence, psychology, engineering, mathematics 3. A basic component of many neural nets: feed-forward neural networks

Kon, Mark

22

Application Of An Artificial Neural Network Model To A Na-K Geothermometer  

Open Energy Info (EERE)

Application Of An Artificial Neural Network Model To A Na-K Geothermometer Application Of An Artificial Neural Network Model To A Na-K Geothermometer Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Journal Article: Application Of An Artificial Neural Network Model To A Na-K Geothermometer Details Activities (3) Areas (2) Regions (0) Abstract: A new geothermometer model is proposed by applying data obtained from a known Na-K geothermometer to an artificial neural network. In this model, Na and K values were implemented as input signals and geothermometers as the output signal. Multi-layer perceptrons and back propagation were used as training algorithms for the artificial neural network. Reservoir temperatures of some geothermal fields in Turkey determined by this method are in accord with those determined from other methods.

23

Classifying Artificial Neural Network Architecture  

Science Journals Connector (OSTI)

Most Artificial Neural Network architecture appears to be heuristic in conception; ... be preferable to develop a formal taxonomy for Neural Networks in order that network size and designs may be matched more ......

Dr J. P. Evans

1990-01-01T23:59:59.000Z

24

Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine  

Science Journals Connector (OSTI)

The ability of an artificial neural network model, using a back propagation learning algorithm, to predict specific fuel consumption and exhaust temperature of a Diesel engine for various injection timings is studied. The proposed new model is compared with experimental results. The comparison showed that the consistence between experimental and the network results are achieved by a mean absolute relative error less than 2%. It is considered that a well-trained neural network model provides fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems.

Adnan Parlak; Yasar Islamoglu; Halit Yasar; Aysun Egrisogut

2006-01-01T23:59:59.000Z

25

Aircraft System Identification Using Artificial Neural Networks  

E-Print Network [OSTI]

Aircraft System Identification Using Artificial Neural Networks Kenton Kirkpatrick , Jim May Jr Artificial Neural Network System Identification, has the advantages of being straightforward with low for aircraft using an artificial neural network (ANN). Artificial neural networks are useful for creating

Valasek, John

26

Can Neural Networks Explain Stuttering?  

Science Journals Connector (OSTI)

Recently, neural networks have received a great deal of attention from various disciplines. [1,2] In medicine neural network technology is considered helpful both as a new way of constructing systems for clini...

Maikl Braamhof

1990-01-01T23:59:59.000Z

27

Rule generation from neural networks  

SciTech Connect (OSTI)

The neural network approach has proven useful for the development of artificial intelligence systems. However, a disadvantage with this approach is that the knowledge embedded in the neural network is opaque. In this paper, we show how to interpret neural network knowledge in symbolic form. We lay down required definitions for this treatment, formulate the interpretation algorithm, and formally verify its soundness. The main result is a formalized relationship between a neural network and a rule-based system. In addition, it has been demonstrated that the neural network generates rules of better performance than the decision tree approach in noisy conditions. 7 refs.

Fu, L. [Univ. of Florida, Gainesville, FL (United States)

1994-08-01T23:59:59.000Z

28

A VALIDATION INDEX FOR ARTIFICIAL NEURAL NETWORKS  

E-Print Network [OSTI]

A VALIDATION INDEX FOR ARTIFICIAL NEURAL NETWORKS Stephen Roberts, Lionel Tarassenko, James Pardey and estimation properties of artificial neural networks. Like many `traditional' statistical techniques & David Siegwart Neural Network Research Group Department of Engineering Science University of Oxford, UK

Roberts, Stephen

29

A hybrid artificial neural network: computer simulation approach for scheduling a flow shop with multiple processors  

Science Journals Connector (OSTI)

Depending on the characteristics of the manufacturing system and production objectives, dispatching rules have different efficiencies. In this regard, a multiattribute combinatorial dispatching (MACD) decision problem for scheduling a flow shop with multiple processors environment is presented in this paper. We propose a hybrid artificial neural network (ANN) simulation approach as a valid and superior alternative for solving the MACD decision problem. ANNs are one of the commonly used meta-heuristics and are a proven tool for solving complex optimisation problems. The hybrid approach is capable of modelling a non-linear and stochastic problem. Feed forward, multilayered neural network meta-models were trained through the back propagation learning algorithm to provide a complex MACD problem. The solution quality is illustrated by a case study from a multilayer ceramic capacitor manufacturing plant. The manufacturing lead times produced by the hybrid ANN simulation model turned out to be as valid and superior to the conventional simulation model.

Ali Azadeh; Arash Naghavi; Mohsen Moghaddam

2011-01-01T23:59:59.000Z

30

An automatically constructed thesaurus for neural network based document categorization  

Science Journals Connector (OSTI)

This paper presents a method for computing a thesaurus from a text corpus, and combined with a revised back-propagation neural network (BPNN) learning algorithm for document categorization. Automatically constructed thesaurus is a data structure that accomplished by extracting the relatedness between words. Neural network is one of the efficient approaches for document categorization. However the conventional BPNN has the problems of slow learning and easy to involve into the local minimum. We use a revised algorithm to improve the conventional BPNN that can overcome these problems. A well constructed thesaurus has been recognized as valuable tool in the effective operation of document categorization, it overcome some problem for the document categorization based on bag of words which ignored the relationship between words. To investigate the effectiveness of our method, we conducted the experiments on the standard Reuter-21578. The experimental results show that the proposed model was able to achieve higher categorization effectiveness as measured by the precision, recall and F-measure.

Cheng Hua Li; Wei Song; Soon Cheol Park

2009-01-01T23:59:59.000Z

31

An assessment of electrical load forecasting using artificial neural network  

Science Journals Connector (OSTI)

The forecasting of electricity demand has become one of the major research fields in electrical engineering. The supply industry requires forecasts with lead times, which range from the short term (a few minutes, hours, or days ahead) to the long term (up to 20 years ahead). The major priority for an electrical power utility is to provide uninterrupted power supply to its customers. Long term peak load forecasting plays an important role in electrical power systems in terms of policy planning and budget allocation. This paper presents a peak load forecasting model using artificial neural networks (ANN). The approach in the paper is based on multi-layered back-propagation feed forward neural network. For annual forecasts, there should be 10 to 12 years of historical monthly data available for each electrical system or electrical buss. A case study is performed by using the proposed method of peak load data of a state electricity board of India which maintain high quality, reliable, historical data providing the best possible results. Model's quality is directly dependent upon data integrity.

V. Shrivastava; R.B. Misra; R.C. Bansal

2012-01-01T23:59:59.000Z

32

Artificial Neural Network Resistance to Incomplete Data  

Science Journals Connector (OSTI)

This paper presents results obtained in experiments related to artificial neural networks. Artificial neural networks have been trained with delta-bar-delta ... the experiment was to observe how long will neural

Magdalena Alicja Tkacz

2006-01-01T23:59:59.000Z

33

Artificial Neural Network Portion of Coil Study  

E-Print Network [OSTI]

Artificial Neural Network Portion of Coil Study LTC William M. Crocoll School of Systems TO ARTIFICIAL NEURAL NETWORKS A neural network is a massively parallel system comprised of many highly of the brain (Dayhoff, 1990). A major task for a neural network is to learn and maintain a model of the world

Putten, Peter van der

34

Semiring Artificial Neural Networks and Weighted Automata  

E-Print Network [OSTI]

Semiring Artificial Neural Networks and Weighted Automata And an Application to Digital Image neural networks and weighted automata. For this task, we introduce semiring artificial neural networks, that is, artificial neural networks which implement the addition and the multiplication of semirings. We

Hoelldobler, Steffen

35

Artificial Bee Colony Training of Neural Networks  

E-Print Network [OSTI]

Artificial Bee Colony Training of Neural Networks John A. Bullinaria and Khulood AlYahya School of artificial Neural Networks (NNs). Of course, there already exist many hybrid neural network learning for optimization, that has previously been applied successfully to the training of neural networks. This paper ex

Bullinaria, John

36

Introduction to Artificial Intelligence Neural Networks  

E-Print Network [OSTI]

Introduction to Artificial Intelligence (G51IAI) Dr Rong Qu Neural Networks #12;G51IAI ­ Introduction to AI Neural Networks Chapter 20 ­ Artificial Intelligence : A Modern Approach (AIMA) Russell ­ Introduction to AI Neural Networks More precisely: Artificial Neural Networks Simulating, on a computer, what

Qu, Rong

37

Misfire detection of a turbocharged diesel engine by using artificial neural networks  

Science Journals Connector (OSTI)

Abstract This study presents a novel misfire detection model of a turbocharged diesel engine by using artificial neural network model. An explicit back propagation neural network has been developed to identify diesel combustion misfire according to the general engine operating parameters. The parameters are selected by using engine fault mode tree analysis. The proposed neural network model has been implemented in MATLAB/Neural Network Toolbox environment. Experimental study then has been performed on a V6 turbocharged diesel engine to get the parameters for both network training and validation purpose. Initial results show that misfire can be captured in most cases, however some mis-detection could happen though the mean square error of the model is satisfied. Furthermore, the in-cycle engine speed variation, a deductive parameter of transient engine speed, is added into the training data, which promotes the final results to full correct detection with no exception. The current study provides a new way to detect the happenings of misfire of turbocharged diesel engine.

Bolan Liu; Changlu Zhao; Fujun Zhang; Tao Cui; Jianyun Su

2013-01-01T23:59:59.000Z

38

IAI : Biological Intelligence and Neural Networks John A. Bullinaria, 2005  

E-Print Network [OSTI]

Intelligent Things? 2. What are Neural Networks? 3. What are Artificial Neural Networks used for? 4. Introduction to Biological Neural Networks 5. Introduction to Artificial Neural Networks 6. Some Current Artificial Neural Networks Applications 7. Limitations of Artificial Neural Network Systems #12;W3-2 How do

Bullinaria, John

39

AITA : Biological Intelligence and Neural Networks John A. Bullinaria, 2003  

E-Print Network [OSTI]

Intelligent Things? 2. What are Neural Networks? 3. What are Artificial Neural Networks used for? 4. Introduction to Biological Neural Networks 5. Introduction to Artificial Neural Networks 6. Some Current Artificial Neural Networks Applications 7. Limitations of Artificial Neural Network Systems #12;W3s1-2 How do

Bullinaria, John

40

A Novel Artificial Neural Network Learning Algorithm  

Science Journals Connector (OSTI)

The unit feedback recursive neural network model which is widely used at present has been analyzed. It makes the unit feedback recursive neural network have the same dynamic process and time delay characterist...

Tinggui Li; Qinhui Gong

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

Advanced battery modeling using neural networks  

E-Print Network [OSTI]

battery models are available today that can accurately predict the performance of the battery system. This thesis presents a modeling technique for batteries employing neural networks. The advantage of using neural networks is that the effect of any...

Arikara, Muralidharan Pushpakam

1993-01-01T23:59:59.000Z

42

Aircraft System Identification Using Artificial Neural Networks  

E-Print Network [OSTI]

Artificial neural networks o Require minimal user input o Easily implemented o Robust to noise o Fast #12 interactions between neurons that pass electrochemical signals between each other Artificial neural networks

Valasek, John

43

Object Oriented Artificial Neural Network Implementations  

E-Print Network [OSTI]

1 Object Oriented Artificial Neural Network Implementations W. Curt Lefebvre Jose C. Principe Neuro artificial neural networks (ANNs). The conven- tion for ANN simulation has been a direct implementation to develop a graphical artificial neural network simulation environment motivated towards the pro- cessing

Slatton, Clint

44

Online learning processes artificial neural networks  

E-Print Network [OSTI]

On­line learning processes in artificial neural networks Tom M. Heskes Bert Kappen Department, The Netherlands. Abstract We study on­line learning processes in artificial neural networks from a general point. Elsevier, pages 199-- 233. #12; On­line learning processes in artificial neural networks 1 1 Introduction 1

Heskes, Tom

45

1 Introduction The artificial neural network discussed in this paper is unlike most other neural network  

E-Print Network [OSTI]

1 1 Introduction The artificial neural network discussed in this paper is unlike most other neural practically all types of artificial neural network architectures were modeled, with varying levels of Connection Patterns for a Dynamic Artificial Neural Network Proceedings of the Combinations of Genetic

Delaware, University of

46

Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks  

Science Journals Connector (OSTI)

This study presents an artificial neural network (ANN) model to predict the torque and brake specific fuel consumption of a gasoline engine. An explicit ANN based formulation is developed to predict torque and brake specific fuel consumption of a gasoline engine in terms of spark advance, throttle position and engine speed. The proposed ANN model is based on experimental results. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. An ANN model based on a back-propagation learning algorithm for the engine was developed. The performance and an accuracy of the proposed ANN model are found satisfactory. This study demonstrates that ANN is very efficient for predicting the engine torque and brake specific fuel consumption. Moreover, the proposed ANN model is presented in explicit form as a mathematical function.

Necla Kara Togun; Sedat Baysec

2010-01-01T23:59:59.000Z

47

Neural modeling of vapor compression refrigeration cycle with extreme learning machine  

Science Journals Connector (OSTI)

In this paper, a single-hidden layer feed-forward neural network (SLFN) is used to model the dynamics of the vapor compression cycle in refrigeration and air-conditioning systems, based on the extreme learning machine (ELM). It is shown that the assignment ... Keywords: Back propagation, Extreme learning machine, Modeling, Radial basis function, Support vector regression, Vapor compression refrigeration cycle

Lei Zhao; Wen-Jian Cai; Zhi-Hong Man

2014-03-01T23:59:59.000Z

48

Identification and control of plasma vertical position using neural network in Damavand tokamak  

SciTech Connect (OSTI)

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

Rasouli, H. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of); Advanced Process Automation and Control (APAC) Research Group, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran (Iran, Islamic Republic of); Rasouli, C.; Koohi, A. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of)

2013-02-15T23:59:59.000Z

49

Prediction of siRNA knockdown efficiency using artificial neural network models  

SciTech Connect (OSTI)

Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.

Ge Guangtao [Department of Computer Science, Tufts University, 161 College Avenue, Medford, MA 02155 (United States) and Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142 (United States)]. E-mail: guge@eecs.tufts.edu; Wong, G.William [Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142 (United States)]. E-mail: wong@wi.mit.edu; Luo Biao [RNAi Consortium, Broad Institute, Massachusetts Institute of Technology, 320 Bent Street, Cambridge, MA 02142 (United States)]. E-mail: bluo@broad.mit.edu

2005-10-21T23:59:59.000Z

50

Prediction of Weld Penetration in FCAW of HSLA steel using Artificial Neural Networks  

SciTech Connect (OSTI)

Flux-cored arc welding (FCAW) is a semiautomatic or automatic arc welding process that requires a continuously-fed consumable tubular electrode containing a flux. The main FCAW process parameters affecting the depth of penetration are welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed. Shallow depth of penetration may contribute to failure of a welded structure since penetration determines the stress-carrying capacity of a welded joint. To avoid such occurrences; the welding process parameters influencing the weld penetration must be properly selected to obtain an acceptable weld penetration and hence a high quality joint. Artificial neural networks (ANN), also called neural networks (NN), are computational models used to express complex non-linear relationships between input and output data. In this paper, artificial neural network (ANN) method is used to predict the effects of welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed on weld penetration depth in gas shielded FCAW of a grade of high strength low alloy steel. 32 experimental runs were carried out using the bead-on-plate welding technique. Weld penetrations were measured and on the basis of these 32 sets of experimental data, a feed-forward back-propagation neural network was created. 28 sets of the experiments were used as the training data and the remaining 4 sets were used for the testing phase of the network. The ANN has one hidden layer with eight neurons and is trained after 840 iterations. The comparison between the experimental results and ANN results showed that the trained network could predict the effects of the FCAW process parameters on weld penetration adequately.

Asl, Y. Dadgar; Mostafa, N. B.; Panahizadeh, V. R. [Department of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran (Iran, Islamic Republic of); Seyedkashi, S. M. H. [Department of Mechanical Engineering, Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

2011-01-17T23:59:59.000Z

51

Foundations of Artificial Intelligence Neural Networks  

E-Print Network [OSTI]

Foundations of Artificial Intelligence Neural Networks Building Artificial Brains #12;Background of observed examples (training data). #12;Neural Networks Objectives Show how the human brain works Introduction The Human Brain (How a neuron works) Building Artificial Neurons Network Architecture and Learning

Qu, Rong

52

Oil reservoir properties estimation using neural networks  

SciTech Connect (OSTI)

This paper investigates the applicability as well as the accuracy of artificial neural networks for estimating specific parameters that describe reservoir properties based on seismic data. This approach relies on JPL`s adjoint operators general purpose neural network code to determine the best suited architecture. The authors believe that results presented in this work demonstrate that artificial neural networks produce surprisingly accurate estimates of the reservoir parameters.

Toomarian, N.B. [California Inst. of Tech., Pasadena, CA (United States); Barhen, J.; Glover, C.W. [Oak Ridge National Lab., TN (United States). Center for Engineering Systems Advanced Research; Aminzadeh, F. [UNOCAL Corp., Sugarland, TX (United States)

1997-02-01T23:59:59.000Z

53

The LILARTI neural network system  

SciTech Connect (OSTI)

The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

1992-10-01T23:59:59.000Z

54

Algorithms and Hardware for Implementing Artificial Neural Networks Nathan Hower  

E-Print Network [OSTI]

Algorithms and Hardware for Implementing Artificial Neural Networks Nathan Hower Abstract Complex problems require sophisticated processing techniques. Artificial neural networks are based require a parallel processing approach to be computed at practical speeds. Artificial neural networks

55

Composite Artificial Neural Network for Controlling Artificial Flying Creature  

Science Journals Connector (OSTI)

This paper proposes a composite artificial neural network (CANN). The CANN is a method that contains concepts of an evolutionary artificial neural network, a neural network ensemble and subsumption architecture, ...

Ryosuke Ooe; Ikuo Suzuki; Masahito Yamamoto…

2013-01-01T23:59:59.000Z

56

Calibrating Artificial Neural Networks by Global Optimization  

E-Print Network [OSTI]

Jul 21, 2010 ... Abstract: An artificial neural network (ANN) is a computational model - implemented as a computer program - that is aimed at emulating the key ...

Janos D. Pinter

2010-07-21T23:59:59.000Z

57

Neural network based system for equipment surveillance  

DOE Patents [OSTI]

A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process.

Vilim, Richard B. (Aurora, IL); Gross, Kenneth C. (Bolingbrook, IL); Wegerich, Stephan W. (Glendale Hts., IL)

1998-01-01T23:59:59.000Z

58

EXPERT CONTROL BASED ON NEURAL NETWORKS FOR CONTROLLING  

E-Print Network [OSTI]

neural units in artificial neural networks (ANN), artificial neural network was used to develop a model to control this system, Artificial neural networks and symbolic description methods are introduced. Artificial neural networks are widely applied in greenhouse environmental control to perform some type of non

59

Artificial neural networks in models of specialization, guild evolution  

E-Print Network [OSTI]

Artificial neural networks in models of specialization, guild evolution and sympatric speciation on host choice, employing artificial neural networks as models for the host recognition system

Getz, Wayne M.

60

Acoustic Emission Detection and Prediction of Fatigue Crack Propagation in Composite Patch Repairs Using Neural Networks  

SciTech Connect (OSTI)

An aircraft is subjected to severe structural and aerodynamic loads during its service life. These loads can cause damage or weakening of the structure especially for aging military and civilian aircraft, thereby affecting its load carrying capabilities. Hence composite patch repairs are increasingly used to repair damaged aircraft metallic structures to restore its structural efficiency. This paper presents the results of Acoustic Emission (AE) monitoring of crack propagation in 2024-T3 Clad aluminum panels repaired with adhesively bonded octagonal, single sided boron/epoxy composite patch under tension-tension fatigue loading. Crack propagation gages were used to monitor crack initiation. The identified AE sensor features were used to train neural networks for predicting crack length. The results show that AE events are correlated with crack propagation. AE system was able to detect crack propagation even at high noise condition of 10 Hz loading; that crack propagation signals can be differentiated from matrix cracking signals that take place due to fiber breakage in the composite patch. Three back-propagation cascade feed forward networks were trained to predict crack length based on the number of fatigue cycles, AE event number, and both the Fatigue Cycles and AE events, as inputs respectively. Network using both fatigue cycles and AE event number as inputs to predict crack length gave the best results, followed by Network with fatigue cycles as input, while network with just AE events as input had a greater error.

Okafor, A. Chukwujekwu; Singh, Navdeep; Singh, Navrag [Structural Health Monitoring and NDE Laboratory, Department of Mechanical and Aerospace Engineering, University of Missouri-Rolla, 1870 Miner Circle Rolla MO 65409-0050 (United States)

2007-03-21T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

Analyte species and concentration identification using differentially functionalized microcantilever arrays and artificial neural networks  

SciTech Connect (OSTI)

In the present work, we have performed analyte species and concentration identification using an array of ten differentially functionalized microcantilevers coupled with a back-propagation artificial neural network pattern recognition algorithm. The array consists of ten nanostructured silicon microcantilevers functionalized by polymeric and gas chromatography phases and macrocyclic receptors as spatially dense, differentially responding sensing layers for identification and quantitation of individual analyte(s) and their binary mixtures. The array response (i.e. cantilever bending) to analyte vapor was measured by an optical readout scheme and the responses were recorded for a selection of individual analytes as well as several binary mixtures. An artificial neural network (ANN) was designed and trained to recognize not only the individual analytes and binary mixtures, but also to determine the concentration of individual components in a mixture. To the best of our knowledge, ANNs have not been applied to microcantilever array responses previously to determine concentrations of individual analytes. The trained ANN correctly identified the eleven test analyte(s) as individual components, most with probabilities greater than 97%, whereas it did not misidentify an unknown (untrained) analyte. Demonstrated unique aspects of this work include an ability to measure binary mixtures and provide both qualitative (identification) and quantitative (concentration) information with array-ANN-based sensor methodologies.

Senesac, Larry R [ORNL; Datskos, Panos G [ORNL; Sepaniak, Michael J [ORNL

2006-01-01T23:59:59.000Z

62

Artificial Neural Networks for Prediction of Response to Chemoradiation in HT29 Xenografts  

SciTech Connect (OSTI)

Purpose: To evaluate the feasibility of using neural networks for predicting treatment response by using longitudinal measurements of apparent diffusion coefficient (ADC) obtained from diffusion-weighted magnetic resonance imaging (DWMRI). Methods and Materials: Mice bearing HT29 xenografts were allocated to six treatment groups receiving different combinations of daily chemotherapy and/or radiation therapy for 2 weeks. T{sub 2}-weighted and DWMR images were acquired before treatment, twice during fractionated chemoradiation (at days 4 and 11), and four times after treatment ended (at days 18, 25, 32, and 46). A tumor doubling growth delay (T{sub delay}) value was found for individual xenografts. ADC values and treatment groups (1-6) were used as input to a back propagation neural network (BPNN) to predict T{sub delay}. Results: When treatment group and ADC values from days 0, 4, 11, 18, 25, 32, and 46 were used as inputs to the BPNN, a strong correlation between measured and predicted T{sub delay} values was found (R = 0.731, p < 0.01). When ADC values from days 0, 4, and 11, and the treatment group were used as inputs, the correlation between predicted and measured T{sub delay} was 0.693 (p < 0.01). Conclusions: BPNN was successfully used to predict T{sub delay} from tumor ADC values obtained from HT29 xenografts undergoing fractionated chemoradiation therapy.

Kakar, Manish [Department of Radiation Biology, Institute for Cancer Research, Oslo University Hospital, Oslo (Norway)], E-mail: Manish.Kakar@rr-research.no; Seierstad, Therese [Department of Radiation Biology, Institute for Cancer Research, Oslo University Hospital, Oslo (Norway); Buskerud University College, Department of Health Sciences, Drammen (Norway); Roe, Kathrine [Department of Radiation Biology, Institute for Cancer Research, Oslo University Hospital, Oslo (Norway); Olsen, Dag Rune [Department of Radiation Biology, Institute for Cancer Research, Oslo University Hospital, Oslo (Norway); Department of Physics, University of Oslo, Oslo (Norway)

2009-10-01T23:59:59.000Z

63

Fundamental building blocks for a compact optoelectronic neural network processor  

E-Print Network [OSTI]

The focus of this thesis is interconnects within the Compact Optoelectronic Neural Network Processor. The goal of the Compact Optoelectronic Neural Network Processor Project (CONNPP) is to build a small, rugged neural ...

Ruedlinger, Benjamin Franklin, 1976-

2003-01-01T23:59:59.000Z

64

VLSI Cells Placement Using the Neural Networks  

SciTech Connect (OSTI)

The artificial neural networks have been studied for several years. Their effectiveness makes it possible to expect high performances. The privileged fields of these techniques remain the recognition and classification. Various applications of optimization are also studied under the angle of the artificial neural networks. They make it possible to apply distributed heuristic algorithms. In this article, a solution to placement problem of the various cells at the time of the realization of an integrated circuit is proposed by using the KOHONEN network.

Azizi, Hacene; Zouaoui, Lamri; Mokhnache, Salah [Universite Ferhat Abbas, Faculte des Sciences Laboratoire Optoelectronique et Composants, Setif(Algeria)

2008-06-12T23:59:59.000Z

65

Artificial neural network modelling and multi objective optimisation of hole drilling electro discharge micro machining of invar  

Science Journals Connector (OSTI)

Hole drilling electro discharge micro machining (HD-EDMM) is one of the potential method for creation of micro-holes in difficult to machine electrically conductive workpiece materials. Maintaining quality and accuracy of the drilled micro-holes along with better performance characteristics have always been a challenge for the researchers and manufacturers. Keeping cost and time of manufacturing into consideration, modelling and optimisation of EDMM is required. In this paper, attempts have been made to model the HD-EDMM process using feed forward back propagation neural network (BPNN) and further combined with GRA-based PCA for its optimisation. The developed ANN model and finally optimised results are validated with our own experimentally obtained results. The approach used in the present paper would be extendable to other configuration of EDMM such as milling-EDMM, wire-EDMM and grinding-EDMM.

Rajesh Kumar Porwal; Vinod Yadava; J. Ramkumar

2012-01-01T23:59:59.000Z

66

Exploring Fractional Order Calculus as an Artificial Neural Network Augmentation Samuel Alan Gardner  

E-Print Network [OSTI]

Exploring Fractional Order Calculus as an Artificial Neural Network Augmentation by Samuel Alan....................................................................................... 4 Artificial Neural Networks DESCRIPTION......................................................................... 22 Neural Network

Dyer, Bill

67

Automated Recurrent Neural Network Design of a Neural Controller in a Custom Power Device  

Science Journals Connector (OSTI)

A general purpose implementation of the Tabu Search metaheuristic, called Universal Tabu Search, is used to optimally design a Locally Recurrent Neural Network architecture. Indeed, the design of a neural network is a tedious and time consuming trial ... Keywords: custom power protection device, neural controller, recurrent neural networks, universal Tabu Search

B. Cannas; G. Celli; A. Fanni; F. Pilo

2001-05-01T23:59:59.000Z

68

Tampa Electric Neural Network Sootblowing  

SciTech Connect (OSTI)

Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NOx formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing cofunding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent sootblowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, on-line, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce NO{sub x} emissions and improve heat rate around the clock.

Mark A. Rhode

2004-09-30T23:59:59.000Z

69

Landslide Prediction Based on Neural Network Modelling  

Science Journals Connector (OSTI)

The opportunities of artificial neural networks model application to landslide forecasting are considered, namely prediction of landslide types and parameters of landslide damage area. The data collected by ob...

Yuri Aleshin; Isakbek Torgoev

2013-01-01T23:59:59.000Z

70

Imbibition well stimulation via neural network design  

DOE Patents [OSTI]

A method for stimulation of hydrocarbon production via imbibition by utilization of surfactants. The method includes use of fuzzy logic and neural network architecture constructs to determine surfactant use.

Weiss, William (Socorro, NM)

2007-08-14T23:59:59.000Z

71

1368 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 8, AUGUST 2009 Simple Artificial Neural Networks That Match Probability  

E-Print Network [OSTI]

1368 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 8, AUGUST 2009 Simple Artificial Neural phenomenon called probability matching (Vulkan 2000). Here, we show that a simple artificial neural network a simple artificial neural network can vary response strengths in accordance with such probability matching

Dawson, Michael

72

Parametric Optimization of Artificial Neural Networks for Signal Approximation Applications  

E-Print Network [OSTI]

Parametric Optimization of Artificial Neural Networks for Signal Approximation Applications J. Lane.thames@gatech.edu randal.abler@gatech.edu dirk.schaefer@me.gatech.edu ABSTRACT Artificial neural networks are used to solve set of configuration parameters for artificial neural networks such that the network's approximation

73

Developmental Plasticity in Cartesian Genetic Programming Artificial Neural Networks  

E-Print Network [OSTI]

Developmental Plasticity in Cartesian Genetic Programming Artificial Neural Networks Maryam Mahsal developmental plasticity in Artificial Neural Networks using Carte- sian Genetic Programming. This is inspired a trained artificial neural network loses its accuracy when the network is trained again on a different

Fernandez, Thomas

74

Autoassociative neural networks and noise filtering  

Science Journals Connector (OSTI)

We introduce linear autoassociative neural (AN) network filters for the removal of additive noise from one-dimensional (1-D) time series. The AN network will have a (2M+1)×L×(2M+1) architecture, and for M fixed, we show how to choose ...

J.R. Dorronsoro; V. Lopez; C.S. Cruz; J.A. Siguenza

2003-05-01T23:59:59.000Z

75

Neural networks as perpetual information generators  

Science Journals Connector (OSTI)

The information gain in a neural network cannot be larger than the bit capacity of the synapses. It is shown that the equation derived by Engel et al. [Phys. Rev. A 42, 4998 (1990)] for the strongly diluted network with persistent stimuli contradicts this condition. Furthermore, for any time step the correct equation is derived by taking the correlation between random variables into account.

Harald Englisch; Yegao Xiao; Kailun Yao

1991-07-15T23:59:59.000Z

76

Informatica 17 page xxx--yyy 1 ON BAYESIAN NEURAL NETWORKS  

E-Print Network [OSTI]

and neural networks . An artificial neural network is constructed from a set of artificial neurons connectedInformatica 17 page xxx--yyy 1 ON BAYESIAN NEURAL NETWORKS Igor Kononenko University of Ljubljana neural network, Hopfield's neural network, naive Bayesian classifier, con­ tinuous neural network

Kononenko, Igor

77

Artificial Neural Network on a Massively Parallel Associative Architecture  

Science Journals Connector (OSTI)

An implementation of a fully connected artificial neural network using the multi-layered perceptron model is described. The neural network is implemented on the ASP (Associative String ... Microsystems Ltd., base...

A. Krikelis

1990-01-01T23:59:59.000Z

78

NEURAL NETWORK RESIDUAL STOCHASTIC COSIMULATION FOR ENVIRONMENTAL DATA ANALYSIS  

E-Print Network [OSTI]

on radioactive soil contamination from the Chernobyl fallout. Introduction The problem of analysing environmentalNEURAL NETWORK RESIDUAL STOCHASTIC COSIMULATION FOR ENVIRONMENTAL DATA ANALYSIS V. Demyanov, M original method of stochastic simulation of environmental data -- Neural Network Residual Sequential

79

Self-organizing neural networks for learning air combat maneuvers  

E-Print Network [OSTI]

are incompatible to the self-organizing neural network. Therefore, it has to be flattened and then translatedSelf-organizing neural networks for learning air combat maneuvers Teck-Hou Teng, Ah-Hwee Tan, Yuan platform known as CAE STRIVE R CGF. A self-organizing neural network is used for the adaptive CGF to learn

Tan, Ah-Hwee

80

Artificial Neural Networks for Recognition of Electrocardiographic Lead Reversal  

E-Print Network [OSTI]

Artificial Neural Networks for Recognition of Electrocardiographic Lead Reversal Bo Heden, Iv, which are rule-based,is a diffi- cult task, even for the expert. Artificial neural networks (ANNs) have lack of prop- 100%). The neural networks performed better *an `the er treatment. The pur r se

Peterson, Carsten

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

Using Artificial Neural Networks to Play Pong Luis E. Ramirez  

E-Print Network [OSTI]

Using Artificial Neural Networks to Play Pong Luis E. Ramirez May 9th, 2014 Abstract This paper examines the possibility of using Artificial Neural Networks to control AI for simple computer games Stanley that evolves artificial neural network topologies simultane- ously with the edge weights[3

Meeden, Lisa A.

82

REGULARIZATION OF A PROGRAMMED RECURRENT ARTIFICIAL NEURAL NETWORK  

E-Print Network [OSTI]

REGULARIZATION OF A PROGRAMMED RECURRENT ARTIFICIAL NEURAL NETWORK Andrew J. Meade, Jr. Department ARTIFICIAL NEURAL NETWORK Andrew J. Meade, Jr. Department of Mechanical Engineering and Materials Science into an artificial neural network architecture. GTR provides a rational means of combining theoretical models

Meade, Andrew J.

83

Characterization of Shape Memory Alloys Using Artificial Neural Networks  

E-Print Network [OSTI]

1 Characterization of Shape Memory Alloys Using Artificial Neural Networks Jim Henrickson, Kenton � Shape Memory Alloys � Artificial Neural Networks Process � Implement Shape Memory Alloy Model � Generate Training Data � Train Artificial Neural Network Results Conclusion Characterization of Shape

Valasek, John

84

Combinatorial Optimization with Feedback Artificial Neural Networks \\Lambda  

E-Print Network [OSTI]

Combinatorial Optimization with Feedback Artificial Neural Networks \\Lambda Carsten Peterson@thep.lu.se Abstract A brief review is given for using feedback artificial neural networks (ANN) to obtain good Neural Networks, Oc­ tober 1995, Paris, France , eds. F. Fogelman­Soulie and P. Gallinari, EC2 & Cie

Peterson, Carsten

85

Devices and Circuits for Nanoelectronic Implementation of Artificial Neural Networks  

E-Print Network [OSTI]

Devices and Circuits for Nanoelectronic Implementation of Artificial Neural Networks A Dissertation Implementation of Artificial Neural Networks by ¨Ozg¨ur T¨urel Doctor of Philosophy in Physics and Astronomy. Unfortunately, most artificial neural networks, either software or hardware, do not provide either the speed

86

Novel Artificial Neural Networks For Remote-Sensing Data Classification  

E-Print Network [OSTI]

Novel Artificial Neural Networks For Remote-Sensing Data Classification Xiaoli Tao* and Howard E artificial neural network architectures applied to multi-class classification problems of remote-sensing data. These approaches are 1) a spiking-neural-network model for the partitioning of data into clusters, and 2) a neuron

Michel, Howard E.

87

Extracting Provably Correct Rules from Artificial Neural Networks  

E-Print Network [OSTI]

Extracting Provably Correct Rules from Artificial Neural Networks Sebastian B. Thrun University procedures have been applied successfully to a variety of real­world scenarios, artificial neural networks for extracting symbolic knowledge from Backpropagation­style artificial neural networks. It does

Clausen, Michael

88

Adaptive control based on neural network system identification  

Science Journals Connector (OSTI)

In adaptive control and system identification the self tuning regulator has wide range of applications. Neural network and artificial intelligence have big role in this area. This paper presents adaptive neural network control based on self tuning regulator ... Keywords: adaptive control, neural network, neuro control, self tuning regulator, system identification

Hassan E. A. Ibrahim

2012-02-01T23:59:59.000Z

89

An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data  

E-Print Network [OSTI]

An accurate comparison of methods for quantifying variable importance in artificial neural networks, Joy and Death: Assessing variable contributions in neural networks 2 Abstract Artificial neural elements called artificial neural networks (ANNs). Although ANNs were initially developed to better

Joy, Mike

90

Identification of damage in dome-like structures using hybrid sensor measurements and artificial neural networks  

Science Journals Connector (OSTI)

A damage detection scheme using multi-type sensor-based hybrid sensing and artificial-neural-network- (ANN-) based information processing was developed for dome-like structures used in civil infrastructure. Accelerometers and strain sensors were used to provide a hybrid measurement with the purpose of acquiring rich information associated with structural damage. The optimal placement of multiple sensors was explored so as to capture the most appropriate and sensitive signal features (damage parameter vectors) for damage characterization. A back-propagation ANN was constructed with the inputs extracted from the hybrid measurement. To validate the capacity of the proposed damage identification scheme, finite element analysis was conducted to identify damage in a Schwedler dome structure as an example. The performance of ANNs, trained by three kinds of damage parameter vector extracted from signals captured by (i) a sole accelerometer, (ii) a sole strain sensor, and (iii) both kinds of sensor was compared, to observe that the one trained by hybrid sensor measurement outperformed the others. Error analysis for a series of parametric studies, in which noise at different levels was included in the training input, was further carried out, and robustness of the proposed damage identification scheme under noisy measurement was demonstrated.

Wei Lu; Jun Teng; Youlin Xu; Zhongqing Su

2013-01-01T23:59:59.000Z

91

Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique  

SciTech Connect (OSTI)

Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or overlearning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the testing error achieved was in the order of magnitude of within 10{sup -5} - 10{sup -3}. It was also show that the absolute error achieved by EBaLM-OTR was an order of magnitude better than the lowest error achieved by EBaLM-THP.

Dumidu Wijayasekara; Milos Manic; Piyush Sabharwall; Vivek Utgikar

2011-07-01T23:59:59.000Z

92

Quantum artificial neural networks with applications  

Science Journals Connector (OSTI)

Abstract Since simulations of classical artificial neural networks (CANNs) run on classical computers, the massive parallel processing speed advantage of a neural network is lost. A quantum computer is a computation device that makes direct use of quantum–mechanical phenomena while large-scale quantum computers will be able to solve certain problems much quicker than any classical computer using the best currently known algorithms. Combining the advantages of quantum computers and the idea of CANNs, we propose in this paper a new type of neural networks, named a quantum artificial neural network (QANN), which is presented as a system of interconnected “quantum neurons” which can compute quantum states from input-quantum states by feeding information through the network and can be simulated on quantum computers. To show the ability of approximation of a QANN, we prove a universal approximation theorem (UAT) which reads every continuous mapping that transforms n quantum states as a non-normalized quantum state can be uniformly approximated by a QANN. The UAT implies that \\{QANNs\\} would suggest a potential computing tool for dealing with quantum information. For instance, we prove that the state of a quantum system driven by a time-dependent Hamiltonian can be approximated uniformly by a QANN. This provides a possible way for finding approximate solution to a Schrödinger equation with a time-dependent Hamiltonian.

Huaixin Cao; Feilong Cao; Dianhui Wang

2015-01-01T23:59:59.000Z

93

Toward IMRT 2D dose modeling using artificial neural networks: A feasibility study  

SciTech Connect (OSTI)

Purpose: To investigate the feasibility of artificial neural networks (ANN) to reconstruct dose maps for intensity modulated radiation treatment (IMRT) fields compared with those of the treatment planning system (TPS). Methods: An artificial feed forward neural network and the back-propagation learning algorithm have been used to replicate dose calculations of IMRT fields obtained from PINNACLE{sup 3} v9.0. The ANN was trained with fluence and dose maps of IMRT fields for 6 MV x-rays, which were obtained from the amorphous silicon (a-Si) electronic portal imaging device of Novalis TX. Those fluence distributions were imported to the TPS and the dose maps were calculated on the horizontal midpoint plane of a water equivalent homogeneous cylindrical virtual phantom. Each exported 2D dose distribution from the TPS was classified into two clusters of high and low dose regions, respectively, based on the K-means algorithm and the Euclidian metric in the fluence-dose domain. The data of each cluster were divided into two sets for the training and validation phase of the ANN, respectively. After the completion of the ANN training phase, 2D dose maps were reconstructed by the ANN and isodose distributions were created. The dose maps reconstructed by ANN were evaluated and compared with the TPS, where the mean absolute deviation of the dose and the {gamma}-index were used. Results: A good agreement between the doses calculated from the TPS and the trained ANN was achieved. In particular, an average relative dosimetric difference of 4.6% and an average {gamma}-index passing rate of 93% were obtained for low dose regions, and a dosimetric difference of 2.3% and an average {gamma}-index passing rate of 97% for high dose region. Conclusions: An artificial neural network has been developed to convert fluence maps to corresponding dose maps. The feasibility and potential of an artificial neural network to replicate complex convolution kernels in the TPS for IMRT dose calculations have been demonstrated.

Kalantzis, Georgios; Vasquez-Quino, Luis A.; Zalman, Travis; Pratx, Guillem; Lei, Yu [Radiation Oncology Department, University of Texas, Health Science Center San Antonio, Texas 78229 and Radiation Oncology Department, Stanford University School of Medicine, Stanford, California 94305 (United States); Radiation Oncology Department, University of Texas, Health Science Center San Antonio, Texas 78229 (United States); Radiation Oncology Department, Stanford University School of Medicine, Stanford, California 94305 (United States); Radiation Oncology Department, University of Texas, Health Science Center San Antonio, Texas 78229 (United States)

2011-10-15T23:59:59.000Z

94

An artificial-neural-network method for the identification of saturated turbogenerator parameters based on a coupled finite-element/state-space computational algorithm  

SciTech Connect (OSTI)

An artificial neural network (ANN) is used in the identification of saturated synchronous machine parameters under diverse operating conditions. The training data base for the ANN is generated by a time-stepping coupled finite-element/state-space (CFE-SS) modeling technique which is used in the computation of the saturated parameters of a 20-kV, 733-MVA, 0.85 pf (lagging) turbogenerator at discrete load points in the P-Q capability plane for three different levels of terminal voltage. These computed parameters constitute a learning data base for a multilayer ANN structure which is successfully trained using the back-propagation algorithm. Results indicate that the trained ANN can identify saturated machine reactances for arbitrary load points in the P-Q plane with an error less than 2% of those values obtained directly from the CFE-SS algorithm. Thus, significant savings in computational time are obtained in such parameter computation tasks.

Chaudhry, S.R.; Ahmed-Zaid, S. [Clarkson Univ., Potsdam, NY (United States). Electrical and Computer Engineering Dept.] [Clarkson Univ., Potsdam, NY (United States). Electrical and Computer Engineering Dept.; Demerdash, N.A. [Marquette Univ., Milwaukee, WI (United States). Electrical and Computer Engineering Dept.] [Marquette Univ., Milwaukee, WI (United States). Electrical and Computer Engineering Dept.

1995-12-01T23:59:59.000Z

95

Auto-associative nanoelectronic neural network  

SciTech Connect (OSTI)

In this paper, an auto-associative neural network using single-electron tunneling (SET) devices is proposed and simulated at low temperature. The nanoelectronic auto-associative network is able to converge to a stable state, previously stored during training. The recognition of the pattern involves decreasing the energy of the input state until it achieves a point of local minimum energy, which corresponds to one of the stored patterns.

Nogueira, C. P. S. M.; Guimarães, J. G. [Departamento de Engenharia Elétrica - Laboratório de Dispositivos e Circuito Integrado, Universidade de Brasília, CP 4386, CEP 70904-970 Brasília DF (Brazil)

2014-05-15T23:59:59.000Z

96

Automated Interpretation of Myocardial SPECT Perfusion Images Using Artificial Neural Networks  

E-Print Network [OSTI]

Automated Interpretation of Myocardial SPECT Perfusion Images Using Artificial Neural Networks Dan as inputs to multilayer perceptron artificial neural networks. The networks were trained to detect coronary% and not statistically significant. Conclusions: Artificial neural networks can detect coronary artery disease

Peterson, Carsten

97

Applications of artificial neural networks predicting macroinvertebrates in freshwaters  

E-Print Network [OSTI]

Applications of artificial neural networks predicting macroinvertebrates in freshwaters Peter L. M Artificial neural networks (ANNs) are non-linear mapping structures that can be applied for predictive P. L suitability models can be very valuable. Data driven methods such as artificial neural net- works (ANNs

Lek, Sovan

98

E-Print Network 3.0 - adaptive neural fuzzy Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Neural Jeremy Binfet Summary: Microprocessor Implementation of Fuzzy Systems and Neural Networks Jeremy Binfet Micron Technology... . Control surfaces obtained from neural...

99

Knowledge Based Descriptive Neural Networks Department of Computer Science,  

E-Print Network [OSTI]

keep the good feature of nonlinearity in neural networks but also have explanation of underlying made up of simple units configured in a highly interconnected network. Neural networks are normally in such diverse applications as handwriting recognition, medical diagnosis, exchange rate prediction, stock market

Yao, JingTao

100

Performance prediction of a solar thermal energy system using artificial neural networks  

Science Journals Connector (OSTI)

Abstract This paper describes in details an application of artificial neural networks (ANNs) to predict the performance of a solar thermal energy system (STES) used for domestic hot water and space heating application. Experiments were conducted on the STES under a broad range of operating conditions during different seasons and Canadian weather conditions in Ottawa, over the period of March 2011 through December 2012 to assess the system performance. These experimental data were utilised for training, validating and testing the proposed ANN model. The model was applied to predict various performance parameters of the system, namely the preheat tank stratification temperatures, the heat input from the solar collectors to the heat exchanger, the heat input to the auxiliary propane-fired tank, and the derived solar fractions. The back-propagation learning algorithm with two different variants, the Levenberg–Marguardt (LM) and scaled conjugate gradient (SCG) algorithms were used in the network. It was found that the optimal algorithm and topology were the LM and the configuration with 10 inputs, 20 hidden and 8 output neurons/outputs, respectively. The preheat tank temperature and solar fraction predictions agreed very well with the experimental values using the testing data sets. The \\{ANNs\\} predicted the preheat water tank stratification temperatures and the solar fractions of the STES within less that ±3% and ±10% errors, respectively. The results confirmed the effectiveness of this method and provided very good accuracy even when the input data are distorted with different levels of noise. Moreover, the results of this study demonstrate that the ANN approach can provide high accuracy and reliability for predicting the performance of complex energy systems such as the one under investigation. Finally, this method can also be exploited as an effective tool to develop applications for predictive performance monitoring system, condition monitoring, fault detection and diagnosis of STES.

Wahiba Yaïci; Evgueniy Entchev

2014-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

Ecological Modelling 120 (1999) 313324 Microsatellites and artificial neural networks: tools for the  

E-Print Network [OSTI]

Ecological Modelling 120 (1999) 313­324 Microsatellites and artificial neural networks: tools´rigueux Cedex, France Abstract Artificial Neural Networks (ANN) were applied to microsatellite data (highly rights reserved. Keywords: Artificial Neural Network; Classification; Microsatellites; Stocking; Brown

Lek, Sovan

102

A COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND CLUSTER ANALYSIS FOR TYPING BIOMETRICS  

E-Print Network [OSTI]

A COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND CLUSTER ANALYSIS FOR TYPING BIOMETRICS biometrics, artificial neural networks, cluster analysis, Multi Layer Perceptrons, K- means clustering are clustering techniques and Artificial Neural Networks , in conjunction with data processing to improve

103

Recognizing targets from infrared intensity scan patterns using artificial neural networks  

E-Print Network [OSTI]

Recognizing targets from infrared intensity scan patterns using artificial neural networks Tayfun complicating the localization and recognition process. We employ artificial neural networks to deter- mine differentiation; artificial neural networks; optimal brain surgeon; pattern recognition. Paper 080450R received

Barshan, Billur

104

Experimental and artificial neural network modeling study on soot formation in premixed hydrocarbon flamesq  

E-Print Network [OSTI]

Experimental and artificial neural network modeling study on soot formation in premixed hydrocarbon after the methane flames, respectively. Three-layer, feed- forward type artificial neural networks rights reserved. Keywords: Soot; Hydrocarbon flames; Artificial neural networks 1. Introduction

Senkan, Selim M.

105

AN ARTIFICIAL NEURAL NETWORK BASED TRANSMISSION LOSS ALLOCATION FOR BILATERAL CONTRACTS  

E-Print Network [OSTI]

AN ARTIFICIAL NEURAL NETWORK BASED TRANSMISSION LOSS ALLOCATION FOR BILATERAL CONTRACTS Rezaul to allocate transmission loss to respective transactions. An artificial neural network based transmission loss technique. Keywords: Neural network; transmission loss; loss allocation; bilateral contracts. 1

Saskatchewan, University of

106

Artificial Neural Networks In Electric Power Industry Technical Report of the ISIS Group  

E-Print Network [OSTI]

Artificial Neural Networks In Electric Power Industry Technical Report of the ISIS Group Systems R. E. Bourguet, P. J. Antsaklis, "Artificial Neural Networks in Electric Power Industry. Bourguet, P. J. Antsaklis, "Artificial Neural Networks in Electric Power Industry," Technical Report

Antsaklis, Panos

107

Artificial Neural Networks-Based Diffuse Optical Tomography  

Science Journals Connector (OSTI)

A scheme is developed by applying the artificial neural networks techniques for the reconstruction of optical-property images instead of using forward and inverse procedures. The...

Pan, Min-Chun; Hong, Hsian-An; Chen, Liang-Yu; Pan, Min-Cheng

108

Evolving artificial neural network structure using grammar encoding and colonial competitive algorithm  

Science Journals Connector (OSTI)

Evolving artificial neural network usually refers to network structure evolution leaving the network’s parameters to be trained using conventional ... paper, we present a new method for artificial neural network ...

Maryam Tayefeh Mahmoudi; Fattaneh Taghiyareh…

2013-05-01T23:59:59.000Z

109

E-Print Network 3.0 - active neural circuits Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

to Summary: with high dimensions and dense structure is to employ artificial neural networks based on circuit... a neural network for solving linear programming problems...

110

An artificial neural network for the prediction of immiscible flood performance  

Science Journals Connector (OSTI)

An artificial neural network for the prediction of immiscible flood performance ... Estimating the Isothermal Compressibility Coefficient of Undersaturated Middle East Crudes Using Neural Networks ...

Ridha Gharbi; Mansour Karkoub; Ali ElKamel

1995-09-01T23:59:59.000Z

111

Artificial neural networks in models of specialization, guild evolution and sympatric speciation  

E-Print Network [OSTI]

Artificial neural networks in models of specialization, guild evolution and sympatric speciation artificial neural networks (ANN) as models for the host recognition system in exploiters, illustrate how

Getz, Wayne M.

112

Application of artificial neural network to predict Escherichia coli O157:H7 inactivation on beef surfaces  

Science Journals Connector (OSTI)

Abstract The objective of this study was to develop artificial neural network (ANN) models for quantifying Escherichia coli O157:H7 (E. coli) inactivation due to low-voltage electric current on beef surfaces and to compare them with statistical models for their suitability as a tool for online processing by the meat industry. Modeling techniques with optimal prediction accuracies of E. coli inactivation on meat would not only enhance the meat quality and public perception from a safety perspective, but also improve the marketability of the meat products. The data used in this study were obtained from experiments that measured the percentage (%) of E. coli O157:H7 reduction (output) on beef surfaces when subjected to current (input 1) 300, 600, and 900 mA, duty cycles (input 2) 30, 50, and 70%, and frequency (input 3) 1, 10, and 100 kHz for three treatment times (2, 8, 16 min). Data were subjected to statistical and artificial neural network (ANN) modeling techniques. Data from each input set were sub-partitioned into training, testing, and validation data sets for ANN. Back-propagation (BP) and Kalman filter (KF) learning algorithms were used in ANN to develop nonparametric models between input and output data sets. The trained ANN models were cross-tested with validation data. Various statistical indices including R2 between actual and predicted outputs were produced and examined for selecting the best networks. Prediction plots for current, frequencies, and duty cycles indicated that ANN models had better accuracies compared to the statistical models in predicting from unseen pattern. Further, ANN models were able to more robustly generalize and interpolate unseen patterns within the domain of training. Since ANN models have the inherent ability to handle high biological variability and the uncertainty associated with inactivation of microorganisms, they have great potential for meat quality evaluation and monitoring in meat industry.

Ramana Gosukonda; Ajit K. Mahapatra; Xuanli Liu; Govind Kannan

2015-01-01T23:59:59.000Z

113

An artificial neural network representation for artificial organisms  

Science Journals Connector (OSTI)

We introduce an artificial neural network (ANN) representation that supports the evolution of complex behaviors in artificial organisms. The strength and location of each connection in the network is specified by...

Robert J. Collins; David R. Jefferson

1991-01-01T23:59:59.000Z

114

Artificial Neural Network Based Modeling of Glucose Metabolism  

Science Journals Connector (OSTI)

Neural network the number of hidden neurons for the network performance has a significant impact, usually for a specific problem, there is no way to determine a certain level in the end should be hidden togeth...

Wangping Xiong; Jianqiang Du; Qinglong Shu…

2011-01-01T23:59:59.000Z

115

Neural-Network-Based Maintenance Decision Model for Diesel Engine  

Science Journals Connector (OSTI)

To decrease the fuzzy and uncertain factors in the maintenance decision models of diesel engine, a combination BP-neural-network-based maintenance decision model for diesel engine is presented in this paper. It can make the maintenance of diesel engine ... Keywords: Deterioration degree, Diesel engine, Maintenance decision, Neural network

Yingkui Gu; Juanjuan Liu; Shuyun Tang

2008-09-01T23:59:59.000Z

116

Multimodal medical image fusion using autoassociative neural network  

Science Journals Connector (OSTI)

In this paper, a principal component extraction based image fusion technique, using auto-associative neural network, has been implemented and analyzed. Fusion of images taken at different resolutions, intensity and by different techniques, helps physicians ... Keywords: RLS learning, auto-associative neural network, image fusion

Suprava Patnaik; Tapasmini Sahoo

2008-04-01T23:59:59.000Z

117

Modeling of solar energy for Malaysia using artificial neural networks  

Science Journals Connector (OSTI)

This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global solar irradiation. The ANN model is based on the feed forward multilayer perception model ... Keywords: Malaysia, artificial neural network, solar energy, solar energy prediction

Tamer Khatib; Azah Mohamed; K. Sopian; M. Mahmoud

2011-10-01T23:59:59.000Z

118

Safety Criteria and Safety Lifecycle for Artificial Neural Networks  

E-Print Network [OSTI]

Safety Criteria and Safety Lifecycle for Artificial Neural Networks Zeshan Kurd, Tim Kelly and Jim performance based techniques that aim to improve the safety of neural networks for safety critical applications. However, many of these techniques provide inadequate forms of safety arguments required

Kelly, Tim

119

Safety Lifecycle for Developing Safety Critical Artificial Neural Networks  

E-Print Network [OSTI]

Safety Lifecycle for Developing Safety Critical Artificial Neural Networks Zeshan Kurd, Tim Kelly. There are many techniques that aim to improve the performance of neural networks for safety-critical systems. Consequently, their role in safety-critical applications, if any, is typically restricted to advisory systems

Kelly, Tim

120

Parallel Training of An Improved Neural Network for Text Categorization  

Science Journals Connector (OSTI)

This paper studies parallel training of an improved neural network for text categorization. With the explosive growth on the amount of digital information available on the Internet, text categorization problem has become more and more important, especially ... Keywords: Neural networks, Parallel computing, Text categorization

Cheng Hua Li, Laurence T. Yang, Man Lin

2014-06-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

Apple Defect Segmentation by Artificial Neural Networks Devrim Unay a  

E-Print Network [OSTI]

Apple Defect Segmentation by Artificial Neural Networks Devrim Unay a Bernard Gosselin a a TCTS Lab-colored apple fruits performed by several artificial neural networks. Pixel-wise classification approach apple defects. 1 Introduction Quality of apple fruits depends on size, color, shape and presence

Dupont, Stéphane

122

Modelling power output at nuclear power plant by neural networks  

Science Journals Connector (OSTI)

In this paper, we propose two different neural network (NN) approaches for industrial process signal forecasting. Real data is available for this research from boiling water reactor type nuclear power reactors. NNs are widely used for time series prediction, ... Keywords: evaluation methods, model input selection, neural networks, nuclear power plant, one-step ahead prediction

Jaakko Talonen; Miki Sirola; Eimontas Augilius

2010-09-01T23:59:59.000Z

123

Arrhythmia Identification from ECG Signals with a Neural Network  

E-Print Network [OSTI]

Arrhythmia Identification from ECG Signals with a Neural Network Classifier Based on a Bayesian a diagnostic system for cardiac arrhythmias from ECG data, using an Artificial Neural Network (ANN) classifier The electrocardiogram (ECG) is the most important biosignal used by cardiol- ogists for diagnostic purposes. The ECG

Madden, Michael

124

Applications of Neural Networks in Hadron Physics  

E-Print Network [OSTI]

The Bayesian approach for the feed-forward neural networks is reviewed. Its potential for usage in hadron physics is discussed. As an example of the application the study of the the two-photon exchange effect is presented. We focus on the model comparison, the estimation of the systematic uncertainties due to the choice of the model, and the over-fitting. As an illustration the predictions of the cross sections ratio $d \\sigma(e^+ p\\to e^+ p)/d \\sigma(e^- p\\to e^- p)$ are given together with the estimate of the uncertainty due to the parametrization choice.

Graczyk, Krzysztof M

2014-01-01T23:59:59.000Z

125

Applications of Neural Networks in Hadron Physics  

E-Print Network [OSTI]

The Bayesian approach for the feed-forward neural networks is reviewed. Its potential for usage in hadron physics is discussed. As an example of the application the study of the the two-photon exchange effect is presented. We focus on the model comparison, the estimation of the systematic uncertainties due to the choice of the model, and the over-fitting. As an illustration the predictions of the cross sections ratio $d \\sigma(e^+ p\\to e^+ p)/d \\sigma(e^- p\\to e^- p)$ are given together with the estimate of the uncertainty due to the parametrization choice.

Krzysztof M. Graczyk; Cezary Juszczak

2014-09-18T23:59:59.000Z

126

Intelligent Servant Robot with Artificial Neural Network  

E-Print Network [OSTI]

Abstract: This work aims at proposing a design of a self learning intelligent porter robot that is constructed with ANN. The basic functionality of the robot includes obstacle avoidance and path learning through its codes as well as ANN. The robot is expected to work in an industrial or domestic environment, where it port objects from one place to another provided place is being instructed to it. The robot is fed with blue print of its working environment. The robot has a skid wheels and an arm of 3 degrees of freedom. Key Words: Robot, Artificial neural network and obstacle I.

Prof Hari; Ram Vishwakarma; M S Ishwarya

127

(Teff,log g,[Fe/H]) Classification of Low-Resolution Stellar Spectra using Artificial Neural Networks  

E-Print Network [OSTI]

New generation large-aperture telescopes, multi-object spectrographs, and large format detectors are making it possible to acquire very large samples of stellar spectra rapidly. In this context, traditional star-by-star spectroscopic analysis are no longer practical. New tools are required that are capable of extracting quickly and with reasonable accuracy important basic stellar parameters coded in the spectra. Recent analyses of Artificial Neural Networks (ANNs) applied to the classification of astronomical spectra have demonstrated the ability of this concept to derive estimates of temperature and luminosity. We have adapted the back-propagation ANN technique developed by von Hippel et al. (1994) to predict effective temperatures, gravities and overall metallicities from spectra with resolving power ~ 2000 and low signal-to-noise ratio. We show that ANN techniques are very effective in executing a three-parameter (Teff,log g,[Fe/H]) stellar classification. The preliminary results show that the technique is even capable of identifying outliers from the training sample.

Shawn Snider; Yuan Qu; Carlos Allende Prieto; Ted von Hippel; Timothy C. Beers; Chistopher Sneden; David L. Lambert

1999-12-19T23:59:59.000Z

128

Application of artificial neural network to study the performance of jig for beneficiation of non-coking coal  

Science Journals Connector (OSTI)

Non-coking coal is the major resource of energy in India. Apart from its utilization in energy sector, the other major application of this coal is in metallurgical sector. The resource of high quality of non-coking coal is not available as per demand; as a result beneficiation of non-coking coal is now becoming essential. Jigging is one of the economical physical beneficiation processes for Indian high ash non-coking coal. At present scenario in coal washery in India, below 3 mm size is not being processed. Attempt has been taken to beneficiate the fine size non-coking coal fractions generated at different sizes of bed materials, feed rates and water rates using laboratory Denver mineral jig. The performance of jig was evaluated in term of Ep and imperfection value. Furthermore artificial neural network (ANN) model has been developed for determining combustible recovery and ash percent of the concentrate. The ANN architecture is made up of three layers (input – hidden – output). A back propagation algorithm was used for training of the ANN model. It has been observed that the predicted values by ANN model are in good agreement with the experimental results.

Lopamudra Panda; A.K. Sahoo; A. Tripathy; S.K. Biswal; A.K. Sahu

2012-01-01T23:59:59.000Z

129

Constraints on adaptation: explaining deviation from optimal sex ratio using artificial neural networks  

E-Print Network [OSTI]

Y Keywords: adaptation; artificial neural networks; evolutionary constraints; parasitoid; sex ratio by modelling information acquisition and processing using artificial neural networks (ANNs) evolving accordingConstraints on adaptation: explaining deviation from optimal sex ratio using artificial neural

West, Stuart

130

Desynchronization of Morris: Lecar Network via Robust Adaptive Artificial Neural Network  

Science Journals Connector (OSTI)

This paper has presented a robust adaptive artificial neural network (ANN) method to desynchronize a network composed of Morris–Lecar (M–L) ... . During the whole process of desynchronizing the network, the robus...

Yingyuan Chen; Jiang Wang; Xile Wei…

2014-01-01T23:59:59.000Z

131

Beneficial role of noise in artificial neural networks  

SciTech Connect (OSTI)

We demonstrate enhancement of neural networks efficacy to recognize frequency encoded signals and/or to categorize spatial patterns of neural activity as a result of noise addition. For temporal information recovery, noise directly added to the receiving neurons allow instantaneous improvement of signal-to-noise ratio [Monterola and Saloma, Phys. Rev. Lett. 2002]. For spatial patterns however, recurrence is necessary to extend and homogenize the operating range of a feed-forward neural network [Monterola and Zapotocky, Phys. Rev. E 2005]. Finally, using the size of the basin of attraction of the networks learned patterns (dynamical fixed points), a procedure for estimating the optimal noise is demonstrated.

Monterola, Christopher [National Institute of Physics, University of the Philippines 1101 Diliman Quezon City (Philippines); Max-Planck Institut fuer Physik Komplexer Systeme Noethnitzerstrasse 38, 01187, Dresden (Germany); Saloma, Caesar [National Institute of Physics, University of the Philippines 1101 Diliman Quezon City (Philippines); Zapotocky, Martin [Max-Planck Institut fuer Physik Komplexer Systeme Noethnitzerstrasse 38, 01187, Dresden (Germany)

2008-06-18T23:59:59.000Z

132

Galaxies, Human Eyes and Artificial Neural Networks  

E-Print Network [OSTI]

Quantitative morphological classification of galaxies is important for understanding the origin of type frequency and correlations with environment. But galaxy morphological classification is still mainly done visually by dedicated individuals, in the spirit of Hubble's original scheme, and its modifications. The rapid increase in data on galaxy images at low and high redshift calls for re-examination of the classification schemes and for new automatic methods. Here we show results from the first systematic comparison of the dispersion among human experts classifying a uniformly selected sample of over 800 digitised galaxy images. These galaxy images were then classified by six of the authors independently. The human classifications are compared with each other, and with an automatic classification by Artificial Neural Networks (ANN). It is shown that the ANNs can replicate the classification by a human expert to the same degree of agreement as that between two human experts.

O. Lahav; A. Naim; R. J. Buta; H. G. Corwin; G. de Vaucouleurs; A. Dressler; J. P. Huchra; S. van den Bergh; S. Raychaudhury; L. Sodre Jr.; M. C. Storrie-Lombardi

1994-12-08T23:59:59.000Z

133

Estimating photometric redshifts with artificial neural networks  

E-Print Network [OSTI]

A new approach to estimating photometric redshifts - using Artificial Neural Networks (ANNs) - is investigated. Unlike the standard template-fitting photometric redshift technique, a large spectroscopically-identified training set is required but, where one is available, ANNs produce photometric redshift accuracies at least as good as and often better than the template-fitting method. The Bayesian priors on the underlying redshift distribution are automatically taken into account. Furthermore, inputs other than galaxy colours - such as morphology, angular size and surface brightness - may be easily incorporated, and their utility assessed. Different ANN architectures are tested on a semi-analytic model galaxy catalogue and the results are compared with the template-fitting method. Finally the method is tested on a sample of ~ 20000 galaxies from the Sloan Digital Sky Survey. The r.m.s. redshift error in the range z < 0.35 is ~ 0.021.

Andrew E. Firth; Ofer Lahav; Rachel S. Somerville

2002-03-15T23:59:59.000Z

134

Estimation of All-Terminal Network Reliability Using an Artificial Neural Network Chat Srivaree-ratana and Alice E. Smith  

E-Print Network [OSTI]

Estimation of All-Terminal Network Reliability Using an Artificial Neural Network Chat Srivaree Reliability Using an Artificial Neural Network STATEMENT OF SCOPE AND PURPOSE When designing computer of All-Terminal Network Reliability Using an Artificial Neural Network ABSTRACT The exact calculation

Smith, Alice E.

135

Neural and Fuzzy Adaptive Control of Induction Motor Drives  

SciTech Connect (OSTI)

This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.

Bensalem, Y. [Research Unit of Modelisation, Analyse, Command of Systems MACS (Tunisia); Sbita, L.; Abdelkrim, M. N. [6029 Universite High School of Engineering-Gabes-Tunisia (Tunisia)

2008-06-12T23:59:59.000Z

136

Phase-response curves and synchronized neural networks  

Science Journals Connector (OSTI)

...Chains of coupled oscillators. In The handbook of brain theory and neural networks...pairs of coupled neural oscillators. In Handbook on dynamical systems: toward applications...adaptation limits PRC-based analysis but does not invalidate it. (iv) The dependence...

2010-01-01T23:59:59.000Z

137

FRAUD DETECTION IN COMMUNICATIONS NETWORKS USING NEURAL AND PROBABILISTIC METHODS  

E-Print Network [OSTI]

FRAUD DETECTION IN COMMUNICATIONS NETWORKS USING NEURAL AND PROBABILISTIC METHODS Michiaki Fraud detection refers to the attempt to detect illegiti- mate usage of a communications network. Three methods to detect fraud are presented. Firstly, a feed-forward neu- ral network based on supervised

Tresp, Volker

138

Physical Parameterization of Stellar Spectra: The Neural Network Approach  

E-Print Network [OSTI]

We present a technique which employs artificial neural networks to produce physical parameters for stellar spectra. A neural network is trained on a set of synthetic optical stellar spectra to give physical parameters (e.g. T_eff, log g, [M/H]). The network is then used to produce physical parameters for real, observed spectra. Our neural networks are trained on a set of 155 synthetic spectra, generated using the SPECTRUM program written by Gray (Gray & Corbally 1994, Gray & Arlt 1996). Once trained, the neural network is used to yield T_eff for over 5000 B-K spectra extracted from a set of photographic objective prism plates (Bailer-Jones, Irwin & von Hippel 1997a). Using the MK classifications for these spectra assigned by Houk (1975, 1978, 1982, 1988) we have produced a temperature calibration of the MK system based on this set of 5000 spectra. It is demonstrated through the metallicity dependence of the derived temperature calibration that the neural networks are sensitive to the metallicity signature in the real spectra. With further work it is likely that neural networks will be able to yield reliable metallicity measurements for stellar spectra.

Coryn A. L. Bailer-Jones; Mike Irwin; Gerard Gilmore; Ted von Hippel

1997-08-22T23:59:59.000Z

139

Artificial Neural Network Technology: for Classification and Cartography of Scientific and Technical Information.  

E-Print Network [OSTI]

Artificial Neural Network Technology: for Classification and Cartography of Scientific Artificial Neural Networks (ANNs) to extend NEURODOC into a neural platform for the cluster analysis with the aid of neural networks (models which are essentially non-linear and threshold-driven). In the design

Paris-Sud XI, Université de

140

A neural network approach to burn-in  

E-Print Network [OSTI]

: Way Kuo (Chair of Committee) Tep Sastri (Member) Robert Shannon (Member) Chanan Singh (Member) Way Kuo Head of Department August 1995 Major Subject; Industrial Enginccring A Neural Network Approach to Bum-in. (August 1995) Nancy L...

Clifford, Nancy Lynn

2012-06-07T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Artificial Neural Network Predictive System for Oxygen Steelmaking Converter  

Science Journals Connector (OSTI)

The main objective of the paper is the presentation of the static control model of steelmaking converter process based on the artificial neural network approach. The results of classical mass and ... regression m...

Jan Falkus; Piotr Pietrzkiewicz; Wojciech Pietrzyk…

2003-01-01T23:59:59.000Z

142

Artificial neural network model for material characterization by indentation  

Science Journals Connector (OSTI)

Analytical methods to interpret the indentation load–displacement curves are difficult to formulate and solve due to material and geometric nonlinearities as well as complex contact interactions. In this study, large strain–large deformation finite element analyses were carried out to simulate indentation experiments. An artificial neural network model was constructed for the interpretation of indentation load–displacement curves. The data from finite element analyses were used to train and validate the artificial neural network model. The artificial neural network model was able to accurately determine the material properties when presented with the load–displacement curves that were not used in the training process. The proposed artificial neural network model is robust and directly relates the characteristics of the indentation load–displacement curve to the elasto-plastic material properties.

K K Tho; S Swaddiwudhipong; Z S Liu; J Hua

2004-01-01T23:59:59.000Z

143

Monte Carlo event reconstruction implemented with artificial neural networks  

E-Print Network [OSTI]

I implemented event reconstruction of a Monte Carlo simulation using neural networks. The OLYMPUS Collaboration is using a Monte Carlo simulation of the OLYMPUS particle detector to evaluate systematics and reconstruct ...

Tolley, Emma Elizabeth

2011-01-01T23:59:59.000Z

144

Unsupervised neural network for forecasting alarms in hydroelectric power plant  

Science Journals Connector (OSTI)

Power plant management relies on monitoring many signals that represent the technical parameters of the real plant. The use of neural networks (NN) is a novel approach that can help to produce decisions when i...

P. Isasi-Viñuela; J. M. Molina-López…

1997-01-01T23:59:59.000Z

145

The Application of Neural Networks to Electric Power Grid Simulation  

Science Journals Connector (OSTI)

A neural network approach is being developed to enable real time simulations for large scale dynamic system simulations of the electric power grid. If the grid is decomposed into several...

Emily T. Swain; Yunlin Xu; Rong Gao…

2006-01-01T23:59:59.000Z

146

Neural network calibration for miniature multi-hole pressure probes  

E-Print Network [OSTI]

A robust and accurate neural network based algorithm phics. for the calibration of miniature multi-hole pressure probes has been developed and a detailed description of its features and use is presented. The code that was developed was intended...

Vijayagopal, Rajesh

1998-01-01T23:59:59.000Z

147

Flexible, High Performance Convolutional Neural Networks for Image Classification  

E-Print Network [OSTI]

in a deep feed-forward network architecture whose output feature vectors are eventually classified. One of the first hierarchi- cal neural systems was the Neocognitron [Fukushima, 1980] which inspired many

Schmidhuber, Juergen

148

NEURAL NETWORKS FOR DISCRETE TOMOGRAPHY K.J. Batenburg a  

E-Print Network [OSTI]

NEURAL NETWORKS FOR DISCRETE TOMOGRAPHY K.J. Batenburg a W.A. Kosters b a Mathematical Institute of crystalline solids at atomic resolution from electron microscopic images can be considered the "holy grail

Kosters, Walter

149

RESERVOIR INFLOW FORECASTING USING NEURAL NETWORKS CHANDRASHEKAR SUBRAMANIAN  

E-Print Network [OSTI]

a mixture of hydroelectric and non- hydroelectric power, the economics of the hydroelectric plants depend, and to economically allocate the load between various non-hydroelectric plants. Neural networks provide an attractive

Manry, Michael

150

Evolution of Memory in Reactive Artificial Neural Networks  

E-Print Network [OSTI]

in the context of evolution: how reactive agents could have evolved into cognitive ones with internalized memory? This study strives to find an answer to the question by simulating neuroevolution on artificial neural networks, with the hypothesis...

Chung, Ji Ryang

2012-07-16T23:59:59.000Z

151

Modelling Power Output at Nuclear Power Plant by Neural Networks  

Science Journals Connector (OSTI)

In this paper, we propose two different neural network (NN) approaches for industrial process signal forecasting. Real data is available for this research from boiling water reactor type nuclear power reactors. N...

Jaakko Talonen; Miki Sirola; Eimontas Augilius

2010-01-01T23:59:59.000Z

152

Higher Order Neural Networks for Satellite Weather Prediction  

Science Journals Connector (OSTI)

Traditional statistical approaches to modeling and prediction have met with only limited success [30]. As a result, researchers have turned to alternative approaches. In this context, Artificial Neural Network...

Ming Zhang; John Fulcher

2004-01-01T23:59:59.000Z

153

A SIMD neural network processor for image processing  

Science Journals Connector (OSTI)

Artificial Neural Networks (ANNs) and image processing requires massively parallel computation of simple operator accompanied by heavy memory access. Thus, this type of operators naturally maps onto Single Instruction Multiple Data (SIMD) stream parallel ...

Dongsun Kim; Hyunsik Kim; Hongsik Kim; Gunhee Han; Duckjin Chung

2005-05-01T23:59:59.000Z

154

Fitting Multidimensional Global Potential Energy Surfaces with Neural Networks  

E-Print Network [OSTI]

, Albuquerque, New Mexico, 87131, USA Potential energy surfaces Mexico. Our method enforces the nuclear permutation symmetry by introducing multiple symmetry Fitting Multidimensional Global Potential Energy Surfaces with Neural Networks Bin Jiang

Maccabe, Barney

155

E-Print Network 3.0 - aco-bp neural network Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Sciences 26 M. A. Sartori and P. J. Antsaklis, "Neural Network Implementations for Control Scheduling," Technical Report 91-04-02, Dept. of Electrical Engineering,...

156

SVC implementation using neural networks for an AC electrical railway  

Science Journals Connector (OSTI)

This paper presents an on-line method for implementation of a static var compensator (SVC) in a real ac autotransformer (AT)-fed electrical railway for reactive power compensation using Neural Networks (NN). Genetic algorithm (GA) can be the off-line ... Keywords: AC electrical railways load flow, forward/backward sweep (FBS), genetic algorithm (GA), neural network (NN), reactive power compensation, static var compensator (SVC)

Saeid Veysi Raygani; Bijan Moaveni; Seyed Saeed Fazel; Amir Tahavorgar

2011-05-01T23:59:59.000Z

157

AUTOMATED DEFECT CLASSIFICATION USING AN ARTIFICIAL NEURAL NETWORK  

SciTech Connect (OSTI)

The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

Chady, T.; Caryk, M. [Szczecin University of Technology, Department of Electrical Engineering (Poland); Piekarczyk, B. [Technic-Control, Szczecin (Poland)

2009-03-03T23:59:59.000Z

158

A portable neural network approach to vehicle tracking  

E-Print Network [OSTI]

A PORTABLE NEURAL NETWORK APPROACH TO VEHICLE TRACKING A Thesis by KELLY MAXWELL MILLER Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May... 1994 Major Subject: Electrical Engineering A PORTABLE NEURAL NETWORK APPROACH TO VEHICLE TRACKING A Thesis by KELLY MAXWELL MILLER Submitted to the OAice of Graduate Studies of Texas ARM University in partial fulfillment of the requirements...

Miller, Kelly Maxwell

2012-06-07T23:59:59.000Z

159

A new acceleration technique for the backpropagation neural network paradigm  

E-Print Network [OSTI]

A NEW ACCELERATION TECHNIQUE FOR THE BACKPROPAGATION NEURAL NETWORK PARADIGM A Thesis by JOGEN I(. PATHAI( Submitted to the Oflice ol Graduate Studies of Texas AkM University in partial fulfillment of the requirements for the degree... of MASTER OF SCIENCE December 1991 Major Subject: Computer Science A NEW ACCELERATION TECHNIQUE FOR THE BACKPROPAGATION NEURAL NETWORK PARADIGM A Thesis by JOGEN K. PATHAK Approved as to style and content by: Bahram Nassersharif (Chair of Committee...

Pathak, Jogen K

1991-01-01T23:59:59.000Z

160

An artificial neural network based groundwater flow and transport simulator  

SciTech Connect (OSTI)

Artificial neural networks are investigated as a tool for the simulation of contaminant loss and recovery in three-dimensional heterogeneous groundwater flow and contaminant transport modeling. These methods have useful applications in expert system development, knowledge base development and optimization of groundwater pollution remediation. The numerical model runs used to develop the artificial neural networks can be re-used to develop artificial neural networks to address alternative optimization problems or changed formulations of the constraints and or objective function under optimization. Artificial neural networks have been analyzed with the goal of estimating objectives which normally require the use of traditional flow and transport codes: such as contaminant recovery, contaminant loss (unrecovered) and remediation failure. The inputs to the artificial neutral networks are variable pumping withdrawal rates at fairly unconstrained 3-D locations. A forward-feed backwards error propagation artificial neural network architecture is used. The significance of the size of the training set, network architecture, and network weight optimization algorithm with respect to the estimation accuracy and objective are shown to be important. Finally, the quality of the weight optimization is studied via cross-validation techniques. This is demonstrated to be a useful method for judging training performance for strongly under-described systems.

Krom, T.D.; Rosbjerg, D.

1998-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

On Teaching Quality Improvement of a Mathematical Topic Using Artificial Neural Networks Modeling (With a Case Study)  

E-Print Network [OSTI]

431 On Teaching Quality Improvement of a Mathematical Topic Using Artificial Neural Networks inspired by simulation by Artificial Neural Networks (ANNs) applied recently for evaluation of phonics performance. Keywords: Artificial Neural Networks, Learning Performance Evaluation, Computer Aided Learning

Spagnolo, Filippo

162

Artificial Neural Network Methods in Quantum Mechanics  

E-Print Network [OSTI]

In a previous article we have shown how one can employ Artificial Neural Networks (ANNs) in order to solve non-homogeneous ordinary and partial differential equations. In the present work we consider the solution of eigenvalue problems for differential and integrodifferential operators, using ANNs. We start by considering the Schr\\"odinger equation for the Morse potential that has an analytically known solution, to test the accuracy of the method. We then proceed with the Schr\\"odinger and the Dirac equations for a muonic atom, as well as with a non-local Schr\\"odinger integrodifferential equation that models the $n+\\alpha$ system in the framework of the resonating group method. In two dimensions we consider the well studied Henon-Heiles Hamiltonian and in three dimensions the model problem of three coupled anharmonic oscillators. The method in all of the treated cases proved to be highly accurate, robust and efficient. Hence it is a promising tool for tackling problems of higher complexity and dimensionality.

I. E. Lagaris; A. Likas; D. I. Fotiadis

1997-05-15T23:59:59.000Z

163

ID3, SEQUENTIAL BAYES, NAIVE BAYES AND BAYESIAN NEURAL NETWORKS  

E-Print Network [OSTI]

to ID3. ID3 learning algorithm (Quinlan 1979) and its successors ACLS (Paterson & Niblett 1982), C4#cient in many learning tasks. It is shown how Sequential Bayes can be transformed into ID3 by replacing of network's execution (Kononenko 1989) enables the us­ age of a neural network as an expert system shell

Kononenko, Igor

164

Strategies for Spectral Profile Inversion using Artificial Neural Networks  

E-Print Network [OSTI]

This paper explores three different strategies for the inversion of spectral lines (and their Stokes profiles) using artificial neural networks. It is shown that a straightforward approach in which the network is trained with synthetic spectra from a simplified model leads to considerable errors in the inversion of real observations. This problem can be overcome in at least two different ways that are studied here in detail. The first method makes use of an additional pre-processing auto-associative neural network to project the observed profile into the theoretical model subspace. The second method considers a suitable regularization of the neural network used for the inversion. These new techniques are shown to be robust and reliable when applied to the inversion of both synthetic and observed data, with errors typically below $\\sim$100 G.

H. Socas-Navarro

2004-10-23T23:59:59.000Z

165

Artificial Neural Network-Based Lot Number Recognition for Cadastral Map  

Science Journals Connector (OSTI)

This paper discusses the implementation of an artificial neural network in detecting lot numbers in a cadastral ... image resizing. A feed-forward with backpropagation artificial neural network was then implement...

Dave E. Marcial; Ed Darcy Dy…

2013-01-01T23:59:59.000Z

166

Learning the neuron functions within a neural network via genetic programming: applications to geophysics and hydrogeology  

Science Journals Connector (OSTI)

A neural network classifier is sought. Classical neural network neurons are aggregations of a weight multiplied by an input value and then controlled via an activation function. This paper learns everything within the neuron using a variant of Genetic ...

Alan J. Barton; Julio J. Valdés; Robert Orchard

2009-06-01T23:59:59.000Z

167

A nonlinear regulator design in the presence of system uncertainties using multilayered neural network  

Science Journals Connector (OSTI)

The authors present a novel nonlinear regulator design method that integrates linear optimal control techniques and nonlinear neural network learning methods. Multilayered neural networks are used to add nonlinear effects to the linear optimal regulator ...

Y. Iiguni; H. Sakai; H. Tokumaru

1991-07-01T23:59:59.000Z

168

A real time model to forecast 24 hours ahead, ozone peaks and exceedance levels. Model based on artificial neural networks, neural classifier and weather predictions.  

E-Print Network [OSTI]

on artificial neural networks, neural classifier and weather predictions. Application in an urban atmosphere - will be solved. Keywords: Artificial neural network; Multilayer Perceptron; ozone modelling; statistical stepwise and Software 22, 9 (2007) 1261-1269" DOI : 10.1016/j.envsoft.2006.08.002 #12;Abstract A neural network combined

Paris-Sud XI, Université de

169

Neural Network Based Energy Storage System Modeling for Hybrid Electric Vehicles  

SciTech Connect (OSTI)

Demonstrates the application of an artificial neural network (ANN) for modeling the energy storage system of a hybrid electric vehicle.

Bhatikar, S. R.; Mahajan, R. L.; Wipke, K.; Johnson, V.

1999-08-01T23:59:59.000Z

170

Neural-network-assisted genetic algorithm applied to silicon clusters  

SciTech Connect (OSTI)

Recently, a new optimization procedure that combines the power of artificial neural-networks with the versatility of the genetic algorithm (GA) was introduced. This method, called neural-network-assisted genetic algorithm (NAGA), uses a neural network to restrict the search space and it is expected to speed up the solution of global optimization problems if some previous information is available. In this paper, we have tested NAGA to determine the ground-state geometry of Si{sub n} (10{<=}n{<=}15) according to a tight-binding total-energy method. Our results indicate that NAGA was able to find the desired global minimum of the potential energy for all the test cases and it was at least ten times faster than pure genetic algorithm.

Marim, L.R.; Lemes, M.R.; Pino, A. Jr. dal [Department of Physics, Instituto Tecnologico de Aeronautica, Pca. Marechal Eduardo Gomes, 50-Sao Jose dos Campos, Sao Paulo 12228-900 (Brazil)

2003-03-01T23:59:59.000Z

171

Neural network definitions of highly predictable protein secondary structure classes  

SciTech Connect (OSTI)

We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been a popular method to attempt to predict these conventional secondary structure classes. Accuracy has been disappointingly low. The algorithm presented here uses neural networks to similtaneously examine both sequence and structure data, and to evolve new classes of secondary structure that can be predicted from sequence with significantly higher accuracy than the conventional classes. These new classes have both similarities to, and differences with the conventional alpha helix, beta strand and coil.

Lapedes, A. [Los Alamos National Lab., NM (United States)]|[Santa Fe Inst., NM (United States); Steeg, E. [Toronto Univ., ON (Canada). Dept. of Computer Science; Farber, R. [Los Alamos National Lab., NM (United States)

1994-02-01T23:59:59.000Z

172

Challenges in recording and stimulation of living neural network based on Original Micro-Electrode Array  

E-Print Network [OSTI]

Challenges in recording and stimulation of living neural network based on Original Micro in bioelectronics can lead to neuroscience applications to explore the properties of neural networks. Micro properties of neural networks. Recent advances in micro and nano technology have opened the way to probe

Paris-Sud XI, Université de

173

Artificial Neural Networks and Hidden Markov Models for Predicting the Protein Structures: The Secondary Structure  

E-Print Network [OSTI]

1 Artificial Neural Networks and Hidden Markov Models for Predicting the Protein Structures advice on the development of this project #12;2 Artificial Neural Networks and Hidden Markov Models learning methods: artificial neural networks (ANN) and hidden Markov models (HMM) (Rost 2002; Karplus et al

174

An Investigation of Artificial Neural Network Architectures in Artificial Life Implementations  

E-Print Network [OSTI]

An Investigation of Artificial Neural Network Architectures in Artificial Life Implementations environments. It is used to examine how different designs for the ants' Artificial Neural Network (ANN) brains aspects of the simulations was to test different artificial neural network structures as the controlling

Güngör, Tunga

175

Engineering Applications of Artificial Intelligence 17 (2004) 227232 Local exponential stability of competitive neural networks with  

E-Print Network [OSTI]

of competitive neural networks with different time scales Anke Meyer-B.asea , Sergei Pilyuginb , Axel Wism This contribution presents a new method of analyzing the dynamics of a biological relevant neural network for analysis and design of these neural networks. r 2004 Elsevier Ltd. All rights reserved. 1. Introduction

Pilyugin, Sergei S.

176

The Holocene 9,5 (1999) pp. 521529 Artificial neural networks and  

E-Print Network [OSTI]

The Holocene 9,5 (1999) pp. 521­529 Artificial neural networks and dendroclimatic reconstructions Platte River basin from tree- ring chronologies using artificial neural networks is explored. The use of artificial neural networks allows a comparison of reconstructions resulting from both linear and nonlinear

Woodhouse, Connie

177

Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks  

E-Print Network [OSTI]

Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks Jürgen Perl, Peter to store these data but to transform them into useful information. Artificial Neural Networks turn out the data. This is the point where Artificial Neural Networks can become extremely helpful: They are able

Perl, Jürgen

178

Evolving artificial neural networks to control chaotic systems Eric R. Weeks* and John M. Burgess  

E-Print Network [OSTI]

Evolving artificial neural networks to control chaotic systems Eric R. Weeks* and John M. Burgess 78712 Received 7 April 1997 We develop a genetic algorithm that produces neural network feedback of unstable fixed points. This is the first dimension- independent algorithm that produces neural network

Weeks, Eric R.

179

MODELING PHYTOPLANKTON ABUNDANCE IN SAGINAW BAY, LAKE HURON: USING ARTIFICIAL NEURAL NETWORKS TO DISCERN FUNCTIONAL INFLUENCE  

E-Print Network [OSTI]

MODELING PHYTOPLANKTON ABUNDANCE IN SAGINAW BAY, LAKE HURON: USING ARTIFICIAL NEURAL NETWORKS; phytoplankton Abbreviations: ANN, artificial neural network; ClÃ? , chloride; DOC, dissolved organic carbon; Kd Phytoplankton abundance, as chl a, in Saginaw Bay, Lake Huron was modeled using arti- ficial neural networks

180

Comparative application of artificial neural networks and genetic algorithms for multivariate time-series modelling  

E-Print Network [OSTI]

Comparative application of artificial neural networks and genetic algorithms for multivariate time of artificial neural networks and genetic algorithms in terms of forecasting and understanding of algal blooms-a, Microcystis, short-term prediction, artificial neural network model, genetic algorithm model, rule sets

Fernandez, Thomas

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

Michel Verleysen Altran 18/11/2002 -1 Artificial Neural Networks  

E-Print Network [OSTI]

1 Michel Verleysen Altran 18/11/2002 - 1 Artificial Neural Networks: general overview and specific: Introduction pwhy "artificial neural networks" (ANN) ? pwhat are ANNs useful for? ? plearning ­ generalization Verleysen Altran 18/11/2002 - 3 Content p Part I: Introduction pwhy "artificial neural networks" (ANN

Verleysen, Michel

182

Prediction of Protein Functional Domains from Sequences Using Artificial Neural Networks  

E-Print Network [OSTI]

Prediction of Protein Functional Domains from Sequences Using Artificial Neural Networks János Ltd., Budapest 1121, Hungary An artificial neural network (ANN) solution is described, are forwarded to feed-forward artificial neural networks with six input and six hidden units with sigmoidal

Szepesvari, Csaba

183

Evolving artificial neural networks to control chaotic systems Eric R. Weeks* and John M. Burgess +  

E-Print Network [OSTI]

Evolving artificial neural networks to control chaotic systems Eric R. Weeks* and John M. Burgess 78712 ~Received 7 April 1997! We develop a genetic algorithm that produces neural network feedback a robust method of train­ ing neural networks to control chaos. The method makes no assumptions about

Weeks, Eric R.

184

May 24, 2012 18:36 manuscript Data Processing using Artificial Neural Networks  

E-Print Network [OSTI]

May 24, 2012 18:36 manuscript Data Processing using Artificial Neural Networks to Improve uncertainties introduced by the 4D-CT scan, we conveniently treated data using artificial neural networks. More step are then used to build a training set for another artificial neural network that learns the lung

Paris-Sud XI, Université de

185

Automatic classification of Sleep Stages on a EEG signal by Artificial Neural Networks  

E-Print Network [OSTI]

Automatic classification of Sleep Stages on a EEG signal by Artificial Neural Networks Nizar of modeling and design to improve the performances of our tool. Key-Words: - Artificial Neural Networks data analysis tools. Concerning this latter point, we have proposed that Artificial Neural Networks

Paris-Sud XI, Université de

186

Ecological Modelling 120 (1999) 6573 Artificial neural networks as a tool in ecological modelling,  

E-Print Network [OSTI]

Ecological Modelling 120 (1999) 65­73 Artificial neural networks as a tool in ecological modelling-34032 Montpellier cedex 1, France Abstract Artificial neural networks (ANNs) are non-linear mapping), genetic algorithms (d'Angelo et al., 1995; Golikov et al., 1995) and artificial neural networks, i.e. ANN

Roche, Benjamin

187

Multi-point tidal prediction using artificial neural network with tide-generating forces  

E-Print Network [OSTI]

Multi-point tidal prediction using artificial neural network with tide-generating forces Hsien Available online 23 June 2006 Abstract This paper presents a neural network model of simulating tides Elsevier B.V. All rights reserved. Keywords: Neural networks; Tides; Tide-generating forces; Harmonic

188

Ecological Modelling 120 (1999) 349358 Use of artificial neural networks for predicting rice crop  

E-Print Network [OSTI]

Ecological Modelling 120 (1999) 349­358 Use of artificial neural networks for predicting rice crop of artificial neural networks (ANN) in predicting presence or absence of flamingo damages from 11 variables B.V. All rights reserved. Keywords: Flamingos; Rice; Damage; Artificial neural networks; Prediction

Lek, Sovan

189

Artificial neural networks in models of specialisation, guild evolution and sympatric speciation  

E-Print Network [OSTI]

11 Artificial neural networks in models of specialisation, guild evolution and sympatric speciation 1/12/10 6:05:26pm page 236 Modelling Perception in Artificial Neural Networks, ed. C. R. Tosh and G artificial neural networks as models for the plant recognition mechanism in insects (Holmgren & Getz,

Getz, Wayne M.

190

Advances in ungauged streamflow prediction using artificial neural networks Lance E. Besaw a  

E-Print Network [OSTI]

Advances in ungauged streamflow prediction using artificial neural networks Lance E. Besaw-John Chang, Associate Editor Keywords: Ungauged streamflow prediction Artificial neural networks Time and test two artificial neural networks (ANNs) to forecast streamflow in unga- uged basins. The model

Vermont, University of

191

Combining Q-Learning with Artificial Neural Networks in an Adaptive Light Seeking Robot  

E-Print Network [OSTI]

Combining Q-Learning with Artificial Neural Networks in an Adaptive Light Seeking Robot Steve Dini an artificial neural network as a function approximator and eliminate the need for an explicit table. 1 limitations of using tables as well as the viability of using artificial neural networks to approximate

Meeden, Lisa A.

192

Automated Interpretation of Myocardial SPECT Perfusion Images Using Artificial Neural Networks  

E-Print Network [OSTI]

Automated Interpretation of Myocardial SPECT Perfusion Images Using Artificial Neural Networks Dan. Conclusion: Artificial neural networks can detect CAD in myocardial bull's-eye scintigrams with such a high significant potential. Key Words: diagnosis; computer-assisted; artificial intelligence; neural networks

Peterson, Carsten

193

Computational subunits of visual cortical neurons revealed by artificial neural networks  

E-Print Network [OSTI]

Computational subunits of visual cortical neurons revealed by artificial neural networks Brian Lau neurons of the cat to spatiotemporal random-bar stimuli and trained artificial neural networks to predict with a simple functional model for complex cells and demonstrate the usefulness of the neural network method

Lau, Brian

194

Zhang, Wang, and Wei 1 An Artificial Neural Network Method for Length-based  

E-Print Network [OSTI]

Zhang, Wang, and Wei 1 An Artificial Neural Network Method for Length-based Vehicle Classification this problem, we develop an artificial neural network method to estimate classified vehicle volumes directly stations on I-5 over a long duration. The results show that the proposed artificial neural network model

Wang, Yinhai

195

Artificial neural network discrimination of black-capped chickadee (Poecile atricapillus) call notes  

E-Print Network [OSTI]

Artificial neural network discrimination of black-capped chickadee (Poecile atricapillus) call; revised 11 April 2006; accepted 12 May 2006 Artificial neural networks were trained to discriminate number s : 43.80Ka., 43.80.Lb, 43.80.Jz JAS Pages: 1111­1117 I. INTRODUCTION An artificial neural network

Dawson, Michael

196

SEQUENTIAL IMPORTANCE SAMPLING BASED ON A COMMITTEE OF ARTIFICIAL NEURAL NETWORKS FOR POSTERIOR HEALTH  

E-Print Network [OSTI]

SEQUENTIAL IMPORTANCE SAMPLING BASED ON A COMMITTEE OF ARTIFICIAL NEURAL NETWORKS FOR POSTERIOR) is used in this study to filter the output distribution from a committee of Artificial Neural Networks at discrete time steps. KEYWORDS : model identification, artificial neural network, committee, sequential

Paris-Sud XI, Université de

197

Stream classification using hierarchical artificial neural networks: A fluvial hazard management tool  

E-Print Network [OSTI]

Stream classification using hierarchical artificial neural networks: A fluvial hazard management-in-Chief Keywords: Stream classification Artificial neural networks Kohonen self-organizing maps Counterpropagation. In this research, we apply non-parametric, clus- tering and classification artificial neural networks to assimilate

Vermont, University of

198

Analysis on equilibrium points of cells in cellular neural networks described using cloning templates  

Science Journals Connector (OSTI)

In the paper, the region of the number of equilibrium points of a cell in cellular neural networks is considered by the relationship between parameters of cellular neural networks. The number of equilibrium points can be obtained by our results, and ... Keywords: Cellular neural networks, Cloning template, Equilibrium point, Stability

Qi Han; Xiaofeng Liao; Tengfei Weng; Chuandong Li; Hongyu Huang

2012-07-01T23:59:59.000Z

199

Application of Neural Network approach for Proton Exchange Membrane fuel cell systems  

Science Journals Connector (OSTI)

Artificial Intelligence (AI) techniques, particularly the Neural Networks (NNs), are recently having significant impact on power electronics. In a Proton Exchange Membrane (PEM) fuel cell system, there is a strong relationship between the available ... Keywords: NNC, PEM fuel cells, dynamic modelling, neural network controllers, neural networks, output variables, performance modelling, power electronics, proton exchange membrane

Mustapha Hatti; Mustapha Tioursi

2009-01-01T23:59:59.000Z

200

Clustering and co-evolution to construct neural network ensembles: An experimental study  

Science Journals Connector (OSTI)

This paper introduces an approach called Clustering and Co-evolution to Construct Neural Network Ensembles (CONE). This approach creates neural network ensembles in an innovative way, by explicitly partitioning the input space through a clustering method. ... Keywords: Clustering, Co-evolution, Evolutionary computation, Neural network ensembles

Fernanda L. Minku; Teresa B. Ludermir

2008-11-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

Dynamic neural network-based fault diagnosis of gas turbine engines  

Science Journals Connector (OSTI)

In this paper, a neural network-based fault detection and isolation (FDI) scheme is presented to detect and isolate faults in a highly nonlinear dynamics of an aircraft jet engine. Towards this end, dynamic neural networks (DNN) are first developed to ... Keywords: Aircraft jet engine, Bank of filters, Computational intelligence, Dynamic neural networks, Fault detection and isolation, Fault diagnosis, Multiple model schemes

S. Sina Tayarani-Bathaie; Z. N. Sadough Vanini; K. Khorasani

2014-02-01T23:59:59.000Z

202

An analog time-multiplexing cellular neural networks computer  

E-Print Network [OSTI]

S T E R OF SCIENCE December 1995 Major Subject: Electrical Engineering AN ANALOG TIME-MULTIPLEXING CELLULAR NEURAL NETWORKS COMPUTER A Thesis by A P O L L O Q U A N F O N G Submitted to Texas A & M University in partial fulfillment... of the requirements for the degree of M A S T E R OF SCIENCE A .D . Patton (Head of Department) December 1995 Major Subject: Electrical Engineering iii ABSTRACT An Analog Time-Multiplexing Cellular Neural Networks Computer. (December 1995) Apollo Quan Fong...

Fong, Apollo Quan

1995-01-01T23:59:59.000Z

203

Organic LEDs for optoelectronic neural networks  

E-Print Network [OSTI]

In this thesis, I investigate the characteristics of Organic Light Emitting Diodes (OLEDs) and assess their suitability for use in the Compact Optoelectronic Integrated Neural (COIN) coprocessor. The COIN coprocessor, a ...

Mars, Risha R

2012-01-01T23:59:59.000Z

204

Potassium diffusive coupling in neural networks  

Science Journals Connector (OSTI)

...symposium on mathematical problems in theoretical physics. Lecture Notes in Physics, no. 30, p. 140. New York, NY: Springer...synchronization: from theory to data analysis. In Handbook of biological physics, neuro-informatics and neural modeling...

2010-01-01T23:59:59.000Z

205

Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks  

SciTech Connect (OSTI)

This is an annual technical report for the work done over the last year (period ending 9/30/2004) on the project titled ''Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks''. The aim of the project is to develop an efficient chemistry model for combustion simulations. The reduced chemistry model will be developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) will be used via a new network topology know as Non-linear Principal Components Analysis (NPCA). We report on the development of a procedure to speed up the training of NPCA. The developed procedure is based on the non-parametric statistical technique of kernel smoothing. When this smoothing technique is implemented as a Neural Network, It is know as Generalized Regression Neural Network (GRNN). We present results of implementing GRNN on a test problem. In addition, we present results of an in house developed 2-D CFD code that will be used through out the project period.

Nelson Butuk

2004-12-01T23:59:59.000Z

206

Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks  

SciTech Connect (OSTI)

This is the final technical report for the project titled 'Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks'. The aim of the project was to develop an efficient chemistry model for combustion simulations. The reduced chemistry model was developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) was used via a new network topology known as Non-linear Principal Components Analysis (NPCA). A commonly used Multilayer Perceptron Neural Network (MLP-NN) was modified to implement NPCA-NN. The training rate of NPCA-NN was improved with the GEneralized Regression Neural Network (GRNN) based on kernel smoothing techniques. Kernel smoothing provides a simple way of finding structure in data set without the imposition of a parametric model. The trajectory data of the reaction mechanism was generated based on the optimization techniques of genetic algorithm (GA). The NPCA-NN algorithm was then used for the reduction of Dimethyl Ether (DME) mechanism. DME is a recently discovered fuel made from natural gas, (and other feedstock such as coal, biomass, and urban wastes) which can be used in compression ignition engines as a substitute for diesel. An in-house two-dimensional Computational Fluid Dynamics (CFD) code was developed based on Meshfree technique and time marching solution algorithm. The project also provided valuable research experience to two graduate students.

Ziaul Huque

2007-08-31T23:59:59.000Z

207

Prediction of flow stress in a wide temperature range involving phase transformation for as-cast Ti–6Al–2Zr–1Mo–1V alloy by artificial neural network  

Science Journals Connector (OSTI)

The isothermal compressions of as-cast Ti–6Al–2Zr–1Mo–1V titanium alloy in a wide temperature range of 1073–1323 K and strain rate range of 0.01–10 s?1 with a reduction of 60% were conducted on a Gleeble-1500 thermo-mechanical simulator. The flow stress shows a complex non-linear intrinsic relationship with strain, strain rate and temperature, meanwhile the strain-softening behavior articulates dynamic recrystallization mechanism in ? phase, dynamic recovery mechanism in ? phase and their comprehensive function during phase transformation (? + ?). Based on the experimental data, an artificial neural network (ANN) was trained with standard back-propagation learning algorithm to generalize the complex deformation behavior characteristics. In the present ANN model, strain and temperature were taken as inputs, and flow stress as output. A comparative study has been made on ANN model and improved Arrhenius-type constitutive model, and their predictability has been evaluated in terms of correlation coefficient (R) and average absolute relative error (ARRE). During ?, ? + ? and ? phase regime, R-value and ARRE-value for the improved Arrhenius-type model are 0.9824% and 6.02%, 0.9644% and 21.02%, and 0.9627% and 12.38%, respectively, while the R-value and ARRE-value for the ANN model are 0.9992% and 0.91%, 0.9996% and 1.47%, and 0.9975% and 2.17%, respectively. The predicted strain–stress curves outside of experimental conditions articulate the similar intrinsic relationships with experimental strain–stress curves. The results show that the feed-forward back-propagation ANN model can accurately tracks the experimental data in a wide temperature range and strain rate range associated with interconnecting metallurgical phenomena, and in further it has a good capacity to model complex hot deformation behavior of titanium alloy outside of experimental conditions.

Guo-zheng Quan; Wen-quan Lv; Yuan-ping Mao; Yan-wei Zhang; Jie Zhou

2013-01-01T23:59:59.000Z

208

Morphological Classification of Galaxies Using Artificial Neural Networks  

E-Print Network [OSTI]

The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS) commissioning data (Shimasaku et al. 2001) with those of supervised artificial neural network programs constructed using the MATLAB Neural Network Toolbox package. Networks in this package have not previously been used for galaxy classification. It is found that simple neural networks are able to improve on the results of linear classifiers, giving correlation coefficients of the order of 0.8 +/- 0.1, compared with those of around 0.7 +/- 0.1 for linear classifiers. The networks are trained using the resilient backpropagation algorithm, which, to the author's knowledge, has not been specifically used in the galaxy classification literature. The galaxy parameters used and the network architecture are both important, and in particular the galaxy concentration index, a measure of the concentration of light towards the centre of the galaxy, is the most significant parameter. Simple networks are briefly applied to 29,429 galaxies with redshifts from the SDSS Early Data Release. They give an approximate ratio of types E/S0:Sp:Irr of 14 +/- 5 : 86 +/- 12 : 0 +/- 0.1, which broadly agrees with the well known approximate ratios of 20:80:1 observed at low redshift.

Nicholas M. Ball

2001-10-22T23:59:59.000Z

209

Artificial Neural Network modelling of sorption chillers  

Science Journals Connector (OSTI)

Abstract Solar cooling is still a young and small but growing market with a large potential. An increasing market development of solar cooling and so-called SolarCombiPlus systems (solar thermal systems providing domestic hot water, space heating and space cooling) can help to reduce primary energy demand and hence emissions of greenhouse gases. To support the market entry and to enhance the market penetration it is important to strengthen consumers’ confidence in these systems. An important aspect for achieving this goal is a standardised method to predict the performance of the complete solar cooling system under real operating conditions. Nonetheless, objective performance test methods are not yet common standard. In this context a performance test method for solar cooling and SolarCombiPlus systems based on the CTSS method (Component Testing – System Simulation) has been developed by the Research and Testing Centre for Thermal Solar Systems (TZS) of the Institute for Thermodynamics and Thermal Engineering (ITW) at the University of Stuttgart within the project “SolTrans”. For the proposed extended CTSS method numerical models are required in order to describe the thermal behaviour of sorption chillers. The main target of the work presented in this paper is dedicated to the development of appropriate models for sorption chillers which can be used for the extended CTSS method. The approach used is the experimental system identification1 In the field of the experimental system identification a mathematical model of a dynamical system (e.g. sorption chiller) is derived from measurements. 1 based on Artificial Neural Networks (ANN). In this approach experimentally measured data are used to derive an ANN model which is able to predict the outlet temperatures of a sorption chiller. In the work presented, measured data of an adsorption chiller were used to develop such an ANN model which is suitable to predict the outlet temperatures of the three hydraulic loops of adsorption chillers. The model was validated with measured data obtained under real operating conditions. The simulated output temperatures show a very good agreement with the measured temperatures.

Patrick Frey; Stephan Fischer; Harald Drück

2014-01-01T23:59:59.000Z

210

Neural network guided search control in partial order planning  

SciTech Connect (OSTI)

The development of efficient search control methods is an active research topic in the field of planning. Investigation of a planning program integrated with a neural network (NN) that assists in search control is underway, and has produced promising preliminary results.

Zimmerman, T. [Arizona State Univ., Tempe, AZ (United States)

1996-12-31T23:59:59.000Z

211

Artificial neural networks in bias dependant noise modeling of MESFETs  

Science Journals Connector (OSTI)

An efficient procedure for accurate noise parameter prediction of microwave MESFETs / HEMTs for various bias conditions is proposed in this paper. It is based on an improved Pospieszalski's noise model. The bias dependences of the noise model elements ... Keywords: MESFET, artificial neural network, bias, noise modeling

Zlatica Marinkovic; Olivera Pronic-Ran?ic; Vera Markovic

2009-08-01T23:59:59.000Z

212

USING NEURAL NETWORKS FOR WEB PROXY CACHE REPLACEMENT  

E-Print Network [OSTI]

USING NEURAL NETWORKS FOR WEB PROXY CACHE REPLACEMENT by JAKE COBB Advisor HALA ELAARAG A senior ................................................................................................................. 22 #12;1 ABSTRACT Web traffic has continued to increase at a significant rate and is resulting in considerable strain on web servers and bandwidth providers, such as Internet Service Providers (ISP). Proxy

Miles, Will

213

Hybrid coupled modeling of the tropical Pacific using neural networks  

E-Print Network [OSTI]

Hybrid coupled modeling of the tropical Pacific using neural networks Shuyong Li, William W. Hsieh To investigate the potential for improving hybrid coupled models (HCM) of the tropical Pacific by the use: dynamical coupled models, statistical models and hybrid coupled models [Barnston et al., 1994]. A hybrid

Hsieh, William

214

Perfect image segmentation using pulse coupled neural networks  

Science Journals Connector (OSTI)

This paper describes a method for segmenting digital images using pulse coupled neural networks (PCNN). The pulse coupled neuron (PCN) model used in PCNN is a modification of the cortical neuron model of Eckhorn et al. (1990). A single layered laterally ...

G. Kuntimad; H. S. Ranganath

1999-05-01T23:59:59.000Z

215

Neural Network-Based Accelerators for Transcendental Function Approximation  

E-Print Network [OSTI]

Neural Network-Based Accelerators for Transcendental Function Approximation Schuyler Eldridge accelerators has the potential to sustain the his- toric energy and performance improvements of computing systems. We propose the use of NN-based accelerators to approximate mathematical functions in the GNU C

Joshi, Ajay

216

2007 Special Issue: CODAM: A neural network model of consciousness  

Science Journals Connector (OSTI)

We present a review of the CODAM neural network control model of consciousness and develop it to arrive at a functional account of consciousness. The main feature is as a speed-up and error-correcting mechanism known, in engineering control theory, to ... Keywords: Attention, Corollary discharge, Efference copy, Pre-reflective self, Working memory

J. G. Taylor

2007-11-01T23:59:59.000Z

217

Forecasting Hospital Bed Availability Using Simulation and Neural Networks  

E-Print Network [OSTI]

Forecasting Hospital Bed Availability Using Simulation and Neural Networks Matthew J. Daniels is a critical factor for decision-making in hospitals. Bed availability (or alternatively the bed occupancy in emergency departments, and many other important hospital decisions. To better enable a hospital to make

Kuhl, Michael E.

218

Suitability of Fuzzy Systems and Neural Networks for Industrial Applications  

E-Print Network [OSTI]

IEEE #12;negative values and the resulted neuron excitation net is calculated as a sum of products within a limited signal range between zero and one. Neural networks can handle basically an unlimited signal range between zero and one. In general, all parameters of fuzzy systems are designed, while

Wilamowski, Bogdan Maciej

219

Author's personal copy Neural Networks 21 (2008) 458465  

E-Print Network [OSTI]

engine, an electric motor and a generator. A highly efficient engine can simultaneously charge Abstract A neural network controller for improved fuel efficiency of the Toyota Prius hybrid electric the appropriate power split between the electric motor and the engine to minimize fuel consumption and emissions

Prokhorov, Danil

220

A New Neural Network Ranker to Evaluate Protein Structure Predictions  

E-Print Network [OSTI]

A New Neural Network Ranker to Evaluate Protein Structure Predictions Davide Ba�u and Gianluca and ranking the quality of a predicted model represents an important and difficult problem in protein three Model Quality Assessment program for protein structure prediction. The novelty of the approach relies

Pollastri, Gianluca

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

Neural Network Applications P.Tino@cs.bham.ac.uk  

E-Print Network [OSTI]

synapses (connections). · 10 orders of magnitude more energy efficient than computer. #12;Plasticity continues for the first 2 years. · Plasticity of the neural network constituents #12;Human Brain · Typically · Plasticity permits nervous system to adapt to its environment. Two mechanisms: (1) creation of new synaptic

Bullinaria, John

222

Neural Networks -Basis P.Tino@cs.bham.ac.uk  

E-Print Network [OSTI]

in the human cortex. 60 trillion synapses (connections). · 10 orders of magnitude more energy efficient than, but dramatic development continues for the first 2 years. · Plasticity of the neural network constituents #12 impose excitation or inhibition, but not both on a receptive neuron. #12;Plasticity · Plasticity permits

Tino, Peter

223

Artificial neural network modeling techniques applied to the hydrodesulfurization process  

Science Journals Connector (OSTI)

Reduction of harmful emissions in the combustion of fossil fuels imposes tighter specifications limiting the sulfur content of fuels. Hydrodesulfurization (HDS) is a key process in most petroleum refineries in which the sulfur is mostly eliminated. The ... Keywords: Hydrodesulfurization, Neural networks, Pollution, Process modeling

Enrique Arce-Medina; José I. Paz-Paredes

2009-01-01T23:59:59.000Z

224

Alternative neural networks to estimate the scour below spillways  

Science Journals Connector (OSTI)

Artificial neural networks (ANN's) are associated with difficulties like lack of success in a given problem and unpredictable level of accuracy that could be achieved. In every new application it therefore becomes necessary to check their usefulness ... Keywords: ANFIS, ANN, Error criteria, RBF, Scour depths, Ski-jump scour

H. Md. Azamathulla; M. C. Deo; P. B. Deolalikar

2008-08-01T23:59:59.000Z

225

Neural Networks for Post-processing Model Output: Caren Marzban  

E-Print Network [OSTI]

variables to the neural network are: Forecast hour, model forecast temperature, relative humidity, wind direction and speed, mean sea level pressure, cloud cover, and precipitation rate and amount. The single to being able to approximate a large class of functions, they are less inclined to overfit data than some

Marzban, Caren

226

Transmission Line Boundary Protection Using Wavelet Transform and Neural Network  

E-Print Network [OSTI]

1 Transmission Line Boundary Protection Using Wavelet Transform and Neural Network Nan Zhang of transmission line protection are: a) differentiating precisely the internal faults from external, and b is verified using frequency-dependent transmission line model and the test results prove its enhanced

227

Neural Information Processing -Letters and Reviews Vol.1, No.1, October, 2003 Artificial Neural Networks as Analytic Tools in an ERP Study of Face  

E-Print Network [OSTI]

memory effects and attest to the utility of ANN's in ERP analysis. Keywords - Artificial neural networks and is LETTER #12;Artificial Neural Networks as Analytic Tools in an ERP Study R. Graham and M.R.W. Dawson 68Neural Information Processing - Letters and Reviews Vol.1, No.1, October, 2003 67 Artificial Neural

Dawson, Michael

228

Predicting stream water quality using artificial neural networks (ANN)  

SciTech Connect (OSTI)

Predicting point and nonpoint source runoff of dissolved and suspended materials into their receiving streams is important to protecting water quality and traditionally has been modeled using deterministic or statistical methods. The purpose of this study was to predict water quality in small streams using an Artificial Neural Network (ANN). The selected input variables were local precipitation, stream flow rates and turbidity for the initial prediction of suspended solids in the stream. A single hidden-layer feedforward neural network using backpropagation learning algorithms was developed with a detailed analysis of model design of those factors affecting successful implementation of the model. All features of a feedforward neural model were investigated including training set creation, number and layers of neurons, neural activation functions, and backpropagation algorithms. Least-squares regression was used to compare model predictions with test data sets. Most of the model configurations offered excellent predictive capabilities. Using either the logistic or the hyperbolic tangent neural activation function did not significantly affect predicted results. This was also true for the two learning algorithms tested, the Levenberg-Marquardt and Polak-Ribiere conjugate-gradient descent methods. The most important step during model development and training was the representative selection of data records for training of the model.

Bowers, J.A.

2000-05-17T23:59:59.000Z

229

Using an artificial neural network to classify black-capped chickadee (Poecile atricapillus) call note types  

E-Print Network [OSTI]

Using an artificial neural network to classify black-capped chickadee (Poecile atricapillus) call each described as a set of 9 summary features. An artificial neural network was trained to identify, a nonlinear statistical method artificial neural net- work and a linear statistical method linear discriminant

Dawson, Michael

230

Review and comparison of methods to study the contribution of variables in artificial neural network models  

E-Print Network [OSTI]

, Greece Abstract Convinced by the predictive quality of artificial neural network (ANN) models in ecology. # 2002 Published by Elsevier Science B.V. Keywords: Artificial neural networks; Backpropagation; NonReview and comparison of methods to study the contribution of variables in artificial neural

Lek, Sovan

231

A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models  

E-Print Network [OSTI]

considerable interest in developing methods for uncertainty analysis of artificial neural network (ANN) models and parametric uncertainty in artificial neural network hydrologic models, Water Resour. Res., 43, W10407, doi:10A simplified approach to quantifying predictive and parametric uncertainty in artificial neural

Chaubey, Indrajeet

232

Fluorescence diagnostics of oil pollution in coastal marine waters by use of artificial neural networks  

E-Print Network [OSTI]

marine waters with fluorescence spectroscopy and of using artificial neural networks for data interpre with an artificial neural network. The results demonstrate the possibility of estimating oil concentrationsFluorescence diagnostics of oil pollution in coastal marine waters by use of artificial neural

Oldenburg, Carl von Ossietzky Universität

233

Enhanced memory performance thanks to neural network assortativity  

SciTech Connect (OSTI)

The behaviour of many complex dynamical systems has been found to depend crucially on the structure of the underlying networks of interactions. An intriguing feature of empirical networks is their assortativity--i.e., the extent to which the degrees of neighbouring nodes are correlated. However, until very recently it was difficult to take this property into account analytically, most work being exclusively numerical. We get round this problem by considering ensembles of equally correlated graphs and apply this novel technique to the case of attractor neural networks. Assortativity turns out to be a key feature for memory performance in these systems - so much so that for sufficiently correlated topologies the critical temperature diverges. We predict that artificial and biological neural systems could significantly enhance their robustness to noise by developing positive correlations.

Franciscis, S. de; Johnson, S.; Torres, J. J. [Departamento de Electromagnetismo y Fisica de la Materia, and Institute Carlos I for Theoretical and Computational Physics, Facultad de Ciencias, University of Granada, 18071 Granada (Spain)

2011-03-24T23:59:59.000Z

234

Artificial Neural Networks for Solving Ordinary and Partial Differential Equations  

E-Print Network [OSTI]

We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the boundary (or initial) conditions and contains no adjustable parameters. The second part is constructed so as not to affect the boundary conditions. This part involves a feedforward neural network, containing adjustable parameters (the weights). Hence by construction the boundary conditions are satisfied and the network is trained to satisfy the differential equation. The applicability of this approach ranges from single ODE's, to systems of coupled ODE's and also to PDE's. In this article we illustrate the method by solving a variety of model problems and present comparisons with finite elements for several cases of partial differential equations.

I. E. Lagaris; A. Likas; D. I. Fotiadis

1997-05-19T23:59:59.000Z

235

A stochastic learning algorithm for layered neural networks  

SciTech Connect (OSTI)

The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given.

Bartlett, E.B. [Iowa State Univ. of Science and Technology, Ames, IA (United States). Dept. of Mechanical Engineering] [Iowa State Univ. of Science and Technology, Ames, IA (United States). Dept. of Mechanical Engineering; Uhrig, R.E. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering] [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering

1992-12-31T23:59:59.000Z

236

A stochastic learning algorithm for layered neural networks  

SciTech Connect (OSTI)

The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given.

Bartlett, E.B. (Iowa State Univ. of Science and Technology, Ames, IA (United States). Dept. of Mechanical Engineering); Uhrig, R.E. (Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering)

1992-01-01T23:59:59.000Z

237

To appear in: Machine Learning: An Artificial Intelligence Approach, volume IV Refining Symbolic Knowledge Using Neural Networks  

E-Print Network [OSTI]

artificial neural networks as part of a multistrategy learning system, there must be a way for neural; 1 Introduction Artificial neural networks (ANNs) have proven to be a powerful and general technique Knowledge Using Neural Networks Geoffrey G. Towell Jude W. Shavlik University of Wisconsin --- Madison 1210

Liblit, Ben

238

Adaptive model predictive process control using neural networks  

DOE Patents [OSTI]

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

Buescher, Kevin L. (Los Alamos, NM); Baum, Christopher C. (Mazomanie, WI); Jones, Roger D. (Espanola, NM)

1997-01-01T23:59:59.000Z

239

Adaptive model predictive process control using neural networks  

DOE Patents [OSTI]

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

Buescher, K.L.; Baum, C.C.; Jones, R.D.

1997-08-19T23:59:59.000Z

240

Neural Network Identification For a C5 Parallel Robot  

SciTech Connect (OSTI)

This paper presents the design and analysis of a neural network-based identification of the inverse dynamic model of a C5 parallel robot. The identification structure is designed using the black box form (the dynamic model is completely unknown). This identification uses real data acquired on the C5 parallel robot by applying a nominal control scheme (PD). The desired trajectories of this scheme are based on Fourier series and the coefficients are chosen in a heuristic way. We have used this type of desired trajectories to obtain exciting trajectories for identification procedure. Three identification schemes are tested and compared. The comparison is performed based on the number of parameters used in each architecture and the quality of the generalization error. The used neural network is of MLP type and composed of one hidden layer.

Daachi, M. E.; Chikouche, D. [Universite Ferhat Abbes, Setif (Algeria); Achili, B. [Laboratoire d'Informatique Avancees de Saint Denis (LIASD) 2, rue de la liberte 93526 Saint Denis Cedex (France); Daachi, B. [Laboratoire Images, Signaux et Systmes Intelligents (LISSI) 122, rue Paul Armangot 94400 Vitry/Seine (France)

2008-06-12T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

Prediction of wastewater treatment plant performance using artificial neural networks  

Science Journals Connector (OSTI)

Artificial neural networks (ANN) models were developed to predict the performance of a wastewater treatment plant (WWTP) based on past information. The data used in this work were obtained from a major conventional treatment plant in the Greater Cairo district, Egypt, with an average flow rate of 1 million m3/day. Daily records of biochemical oxygen demand (BOD) and suspended solids (SS) concentrations through various stages of the treatment process over 10 months were obtained from the plant laboratory. Exploratory data analysis was used to detect relationships in the data and evaluate data dependence. Two ANN-based models for prediction of BOD and SS concentrations in plant effluent are presented. The appropriate architecture of the neural network models was determined through several steps of training and testing of the models. The ANN-based models were found to provide an efficient and a robust tool in predicting WWTP performance.

Maged M Hamed; Mona G Khalafallah; Ezzat A Hassanien

2004-01-01T23:59:59.000Z

242

A Proposed Artificial Neural Network Classifier to Identify Tumor Metastases  

E-Print Network [OSTI]

In this paper we propose a classification scheme to isolate truly benign tumors from those that initially start off as benign but subsequently show metastases. A non-parametric artificial neural network methodology has been chosen because of the analytical difficulties associated with extraction of closed-form stochastic-likelihood parameters given the extremely complicated and possibly non-linear behavior of the state variables.

M. Khoshnevisan; Sukanto Bhattacharya; Florentin Smarandache

2002-12-09T23:59:59.000Z

243

Neural networks in an intelligent user interface to CAD systems  

E-Print Network [OSTI]

or C cells act as simple and complex filters respectively. Fukushima [FUKU88] designed an architecture called the Neocognitron based on the principles mentioned above. In the Neocognitron model, several layers of cells are arranged in a cascade... of the biological neuron. 3. 2. 3 Feed forward A typical artificial neural network is shown in Figure 3. 4. It consists of three layers of neurons. The first, second and third layers are called the input, hidden and output layers respectively. The circles...

Mudigonda, Sunil K

2012-06-07T23:59:59.000Z

244

Laser programmable integrated curcuit for forming synapses in neural networks  

DOE Patents [OSTI]

Customizable neural network in which one or more resistors form each synapse. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength.

Fu, Chi Y. (San Francisco, CA)

1997-01-01T23:59:59.000Z

245

Process for forming synapses in neural networks and resistor therefor  

DOE Patents [OSTI]

Customizable neural network in which one or more resistors form each synapse. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength.

Fu, Chi Y. (San Francisco, CA)

1996-01-01T23:59:59.000Z

246

Neural Networks 23 (2010) 685697 Contents lists available at ScienceDirect  

E-Print Network [OSTI]

Neural Networks 23 (2010) 685­697 Contents lists available at ScienceDirect Neural Networks journal Received in revised form 29 March 2010 Accepted 5 May 2010 Keywords: Micro-electrode array Cell culture Batch processing Connectivity a b s t r a c t Multi-channel acquisition from neuronal networks, either

Arleo, Angelo

247

Artificial Neural Network Modeling of Surface Tension for Pure Organic Compounds  

Science Journals Connector (OSTI)

Artificial Neural Network Modeling of Surface Tension for Pure Organic Compounds ... This study investigates the applicability of artificial neural networks as an efficient tool for the prediction of pure organic compounds’ surface tensions for a wide range of temperatures. ... The most accurate network among several constructed configurations has one hidden layer with 20 neurons. ...

Aliakbar Roosta; Payam Setoodeh; Abdolhossein Jahanmiri

2011-12-09T23:59:59.000Z

248

Character displacement and the evolution of mate choice: an artificial neural network approach  

E-Print Network [OSTI]

Character displacement and the evolution of mate choice: an artificial neural network approach preferences for aspects of conspecific male signals. We used artificial neural network models to simulate varied in their preferences for aspects of conspecific male signals. When we tested networks

Ryan, Michael J.

249

Application of Artificial Neural Network for Estimating Tight Gas Sand Intrinsic Permeability  

Science Journals Connector (OSTI)

Application of Artificial Neural Network for Estimating Tight Gas Sand Intrinsic Permeability ... This jth neuron occupies a general position in the network since it accepts inputs from nodes in the input layer and sends its output to neurons to the second hidden layer. ... (15)?Veelenturf, L. P. J. Analysis and Applications of Artificial Neural Networks; Prentice Hall:? London, 1995. ...

Ali A. Garrouch; Nejib Smaoui

1996-09-19T23:59:59.000Z

250

The application of neural networks with artificial intelligence technique in the modeling of industrial processes  

SciTech Connect (OSTI)

Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

Saini, K. K.; Saini, Sanju [CDLM engg. College Panniwala Mota, Sirsa and Murthal, Sonipat, Haryana (India)

2008-10-07T23:59:59.000Z

251

Artificial neural network for the determination of Hubble Space Telescope aberration from stellar images  

Science Journals Connector (OSTI)

An artificial-neural-network method, first developed for the measurement and control of atmospheric phase distortion, using stellar images, was used to estimate the optical aberration...

Barrett, Todd K; Sandler, David G

1993-01-01T23:59:59.000Z

252

An Artificial Neural Network Based on the Architecture of the Cerebellum for Behavior Learning  

Science Journals Connector (OSTI)

In the last decade, artificial intelligence (AI) pervades every aspect of ... and generalize sensory information. We propose an Artificial Neural Network (ANN) model based on that architecture....

Kenji Iwadate; Ikuo Suzuki; Michiko Watanabe…

2014-01-01T23:59:59.000Z

253

Artificial neural networks applied to the analysis of synchrotron nuclear resonant scattering data  

Science Journals Connector (OSTI)

The capabilities of artificial neural networks for the automatic and instantaneous analysis of nuclear resonant scattering spectra obtained at a synchrotron source are discussed.

Planckaert, N.

2009-12-08T23:59:59.000Z

254

Artificial neural network method for determining optical properties from double-integrating-spheres measurements  

Science Journals Connector (OSTI)

Accurate measurement of the optical properties of biological tissue is very important for optical diagnosis and therapeutics. An artificial neural network (ANN)-based inverse...

Li, Chenxi; Zhao, Huijuan; Wang, Qiuyin; Xu, Kexin

2010-01-01T23:59:59.000Z

255

9.641J / 8.594J Introduction to Neural Networks, Fall 2002  

E-Print Network [OSTI]

Organization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation ...

Seung, H. Sebastian

256

Learning in a hierarchical neural network  

E-Print Network [OSTI]

') = Ue r(h', n'), if Ue(h, n) = max;~?Ur(h, i) (12) 0, otherwise. f ql Ve(h), if Ue(h, n) = msx, ~?Ue(h, i); ). ' otherwise. As the equations note, only the strongest firing cell in each hyperlayer has any of its synapses modified. This algorithim...(num) ? 8) (20) 1(x) 0 f & 0 (21) When any 1(num) cell fires, then at the next time iteration in the network simulation, all the neuron outputs, including the t(num) cells are set to zero. This zeroing of the network is useful during learning in order...

Michaelis, Matthew Clinton

2012-06-07T23:59:59.000Z

257

High-Performance Torque Control for Switched Reluctance Motor Based on Online Fuzzy Neural Network Modeling  

Science Journals Connector (OSTI)

A novel high performance torque control scheme for switched reluctance motors(SRMs) is proposed based on online fuzzy neural network modeling and adaptive sliding-mode current control. Firstly, an adaptive neural fuzzy inference system(ANFIS) is designed ... Keywords: switched reluctance motor, torque control, adaptive neural fuzzy inference system, adaptive sliding mode control

Xuelian Yao; Ruiyun Qi; Zhiquan Deng; Jun Cai

2010-10-01T23:59:59.000Z

258

Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks  

SciTech Connect (OSTI)

This is an annual technical report for the work done over the last year (period ending 9/30/2005) on the project titled ''Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks''. The aim of the project is to develop an efficient chemistry model for combustion simulations. The reduced chemistry model will be developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) will be used via a new network topology know as Non-linear Principal Components Analysis (NPCA). We report on the significant development made in developing a truly meshfree computational fluid dynamics (CFD) flow solver to be coupled to NPCA. First, the procedure of obtaining nearly analytic accurate first order derivatives using the complex step method (CSM) is extended to include computation of accurate meshfree second order derivatives via a theorem described in this report. Next, boosted generalized regression neural network (BGRNN), described in our previous report is combined with CSM and used to obtain complete solution of a hard to solve wave dominated sample second order partial differential equation (PDE): the cubic Schrodinger equation. The resulting algorithm is a significant improvement of the meshfree technique of smooth particle hydrodynamics method (SPH). It is suggested that the demonstrated meshfree technique be termed boosted smooth particle hydrodynamics method (BSPH). Some of the advantages of BSPH over other meshfree methods include; it is of higher order accuracy than SPH; compared to other meshfree methods, it is completely meshfree and does not require any background meshes; It does not involve any construction of shape function with their associated solution of possibly ill conditioned matrix equations; compared to some SPH techniques, no equation for the smoothing parameter is required; finally it is easy to program.

Nelson Butuk

2006-09-21T23:59:59.000Z

259

New Approach for Feature Selection of Thermomechanically Processed HSLA Steel using Pruned-Modular Neural Networks  

Science Journals Connector (OSTI)

A new approach has been used in modeling of strength and ductility of high strength low alloy (HSLA) steel, where a comparative study among fully-connected neural network, modular network and pruned-module arc...

Prasun Das; Avishek Ghosh…

2012-10-01T23:59:59.000Z

260

Artificial Neural Network Estimator Design for the Inferential Model Predictive Control of an Industrial Distillation Column  

Science Journals Connector (OSTI)

The ANN architecture is a multilayer perceptron (MLP), which is a typical feed-forward (layered) neural network.2 A collection of neurons connected to each other forms the artificial neural network. ... It is shown that the how artificial neural networks can model the column, and demonstrated that the network model is as good or better than a simplified first principles model when used for model predictive control. ... A dynamic, nonlinear, multi-input multi-output application using the recurrent dynamic neuron network (RDNN) model is presented for a two-by-two distn. ...

Alm?la Bahar; Canan Özgen; Kemal Leblebicio?lu; U?ur Hal?c?

2004-08-12T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

In International Journal of Neural Networks: Research and Applications, vol. 2, No. 2/3/4, pp.123133, 1992. A Survey of Neural Network Research and Fielded Applications  

E-Print Network [OSTI]

­propagation, CPN: Counter­propagation network, RCE: Restricted Coulomb Energy, Own: Network designed specifically. This includes the frequency of errors after training and comparison of neural network accuracy and speed to achieve various performance levels. 7. Project Status. We give (1) the simulation size, (2) the simulation

Martinez, Tony R.

262

Using Feedforward Neural Networks and Forward Selection of Input Variables for an Ergonomics Data  

E-Print Network [OSTI]

Using Feedforward Neural Networks and Forward Selection of Input Variables for an Ergonomics Data-gradient algorithm to develop an FNN. This article presents an incremental step in the use of FNNs for ergonomics enhancing the effectiveness of the use of neural networks when observations are missing from ergonomics

Kaber, David B.

263

AN ARTIFICIAL NEURAL NETWORK FOR DIMENSIONS AND COST MODELLING OF INTERNAL MICRO-CHANNELS  

E-Print Network [OSTI]

AN ARTIFICIAL NEURAL NETWORK FOR DIMENSIONS AND COST MODELLING OF INTERNAL MICRO Engineering, Dublin City University, Dublin, Ireland ABSTRACT For micro-channel fabrication using laser micro evaluation. Artificial Neural Network (ANN) is one of the numerical methodologies that can be utilised

Lee, Hyowon

264

Health monitoring of FRP using acoustic emission and artificial neural networks  

Science Journals Connector (OSTI)

In this study, a procedure is proposed for damage identification and discrimination for composite materials based on acoustic emission signals clustering using artificial neural networks. An unsupervised methodology based on the self-organizing map of ... Keywords: Acoustic emission, Artificial neural networks, Clustering, Composite materials, Damage, Health monitoring, Waveform analysis

R. de Oliveira; A. T. Marques

2008-02-01T23:59:59.000Z

265

An Artificial Neural Network Classification Approach for use the Ultrasound in Physiotherapy  

Science Journals Connector (OSTI)

In this study, a classification to be used in physiotherapy was realized by means of Artificial Neural Network (ANN). The aim of the classification was to determine the treatment length and appropriate ultrasound value for the age of physiotherapy patients, ... Keywords: Artificial neural network, Ultrasonic therapy, Ultrasound

Hakan I?ik; Sema Arslan

2011-12-01T23:59:59.000Z

266

Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks  

Science Journals Connector (OSTI)

This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for ... Keywords: accuracy, local search, multiclassification, multiobjective evolutionary algorithms, neural networks, sensitivity

Juan Carlos Fernández Caballero; Francisco José Martínez; César Hervás; Pedro Antonio Gutiérrez

2010-05-01T23:59:59.000Z

267

Phase synchronization and chaotic dynamics in Hebbian learned artificial recurrent neural networks  

E-Print Network [OSTI]

Phase synchronization and chaotic dynamics in Hebbian learned artificial recurrent neural networks: increasing the storing capacity of recurrent neural networks as much as possible and observing and studying Colin Molter, Utku Salihoglu and Hugues Bersini Laboratory of artificial intelligence IRIDIA cp194

Molter, Colin

268

Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features  

Science Journals Connector (OSTI)

In this study, we present an application of neural network and image processing techniques for detecting and classifying Phalaenopsis seedling diseases, including bacterial soft rot (BSR), bacterial brown spot (BBS), and Phytophthora black rot (PBR). ... Keywords: Color features, Image processing, Neural network, Phalaenopsis seedling diseases, Texture features, The adjustable exponential transform (AET)

Kuo-Yi Huang

2007-05-01T23:59:59.000Z

269

Artificial Neural Network Analysis for Evaluation of Peptide MS/MS Spectra in Proteomics  

Science Journals Connector (OSTI)

An artificial neural network, based on multilayer perceptron and comprising 10 artificial neurons in the input layer, 23, 10, and 7 neurons in three consecutive hidden layers, and a single neuron in the output layer, was used. ... Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses ...

Tomasz Ba?czek; Adam Buci?ski; Alexander R. Ivanov; Roman Kaliszan

2004-02-05T23:59:59.000Z

270

Application of BP neural network in evaluating e-business performance for service industry  

Science Journals Connector (OSTI)

The BP neural network model has a convergence and self-adaptability. Based on BP neural network algorithms, we establish the prediction system of e-business performance for Chinese service industry. According to our former studies, the e-business performance ...

Maomao Chi; Jing Zhao

2012-04-01T23:59:59.000Z

271

Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial1 Neural Network2  

E-Print Network [OSTI]

Transitional Modeling of Building Heating Energy Demand Using Artificial1 Neural Network2 Subodh Paudel a.Lecorre@mines-nantes.fr9 Abstract10 This paper presents the building heating demand prediction model with occupancy profile Institution15 building and compared its results with static and other pseudo dynamic neural network models

Paris-Sud XI, Université de

272

Suitability of artificial neural networks for designing LoC circuits  

Science Journals Connector (OSTI)

The simulation of complex LoC (Lab-on-a-Chip) devices is a process that requires solving computationally expensive partial differential equations. An interesting alternative uses artificial neural networks for creating computationally feasible models ... Keywords: LoC, MOR, artificial neural networks, lab-on-a-chip, microfluidic devices, nanofluidic devices

David Moreno; Sandra Gómez; Juan Castellanos

2011-06-01T23:59:59.000Z

273

A neural network approach to the selection of feed mix in the feed industry  

Science Journals Connector (OSTI)

Due to frequent changes of feed mix, the anticipation of pellet quality becomes a cumbersome task for a mill. This paper suggests that the artificial neural network can be used to predict the production rate and percentage of dust for a particular mill. ... Keywords: Artificial neural network, Feed cost, Least cost formulation, Pelleting cost, Pelleting rate

Supachai Pathumnakul; Kullapapruk Piewthongngam; Arthit Apichottanakul

2009-08-01T23:59:59.000Z

274

Energy Management System for an Hybrid Electric Vehicle, Using Ultracapacitors and Neural Networks  

E-Print Network [OSTI]

Energy Management System for an Hybrid Electric Vehicle, Using Ultracapacitors and Neural Networks management system for hybrid electric vehicles (HEV), using neural networks (NN), was developed and tested. The system minimizes the energy requirement of the vehicle and can work with different primary power sources

Catholic University of Chile (Universidad Católica de Chile)

275

Artificial neural network ensemble approach for creating a negotiation model with ethical artificial agents  

Science Journals Connector (OSTI)

Negotiation is one of the most prevalent methods that agents, in a multi-agent system, use to reach agreements. Nowadays, one important aspect of negotiation is moral behaviors of agents that involve in negotiation. For this reason, we propose an ethical ... Keywords: artificial neural network, artificial neural networks ensemble, ensemble method, ethical agent, ethical reasoning, intelligent agent, negotiation

Banafsheh Rekabdar; Mahmood Joorabian; Bita Shadgar

2012-04-01T23:59:59.000Z

276

Determination of barreling curve in upsetting process by artificial neural networks  

Science Journals Connector (OSTI)

In this paper, an approach for prediction deformation of upsetting processes is developed. The approach combines the finite element method and Neural Network to view the resultant deformation changes in upsetting. Because real time deformation simulation ... Keywords: FEM, barreling, neural network(NN), prediction, train, upsetting

H. Mohammadi Majd; M. Poursina; K. H. Shirazi

2009-09-01T23:59:59.000Z

277

An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks  

Science Journals Connector (OSTI)

The objective of this paper is to develop an integrated approach using artificial neural networks (ANN) and genetic algorithms (GA) for cost optimization of bridge deck configurations. In the present work, ANN is used to predict the structural design ... Keywords: Artificial neural networks, Constraints, Cost optimization, Genetic algorithm, Grillage analogy, Objective function, T-girder bridge

V. Srinivas; K. Ramanjaneyulu

2007-07-01T23:59:59.000Z

278

Artificial neural network prediction of ischemic tissue fate in acute stroke imaging  

E-Print Network [OSTI]

Artificial neural network prediction of ischemic tissue fate in acute stroke imaging Shiliang Huang Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict

Duong, Timothy Q.

279

Fault Diagnosis System for a Multilevel Inverter Using a Principal Component Neural Network Surin Khomfoi  

E-Print Network [OSTI]

Fault Diagnosis System for a Multilevel Inverter Using a Principal Component Neural Network Surin system in a multilevel-inverter using a compact neural network is proposed in this paper. It is difficult to diagnose a multilevel-inverter drive (MLID) system using a mathematical model because MLID systems consist

Tolbert, Leon M.

280

Modeling of switched reluctance motors based on optimized BP neural networks with parallel chaotic search  

Science Journals Connector (OSTI)

Precise modeling of switched reluctant motor (SRM) is important of switched reluctant motor driving system. In the article, modeling of SRM by a BP neural network with parallel chaotic search (PCS) is presented firstly. Here parallel chaotic search is ... Keywords: BP neural network, optimize, parallel chaotic search, switched reluctant motor

Yong Cheng; Hui Lin

2010-03-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

Whole-brain neural network analysis (connectomics) using cell lineage-based neuron-labeling method  

Science Journals Connector (OSTI)

......and Macro-scale by Microscopy Whole-brain neural network analysis (connectomics...Bunkyo-ku, Tokyo, 113-0032, Japan The brain is a computing machine that receives input...wiring network of neural connections in the brain, which is recently called the connectomics......

Kei Ito; Masayoshi Ito

2014-11-01T23:59:59.000Z

282

Dynamic Positioning System of Semisubmersible Drilling Platform with a T-S Fuzzy Neural Network Controller  

Science Journals Connector (OSTI)

Position-keeping of a semi submersible drilling platform is an important matter in a production system in the deep sea. It is a key problem how to keep platform stationary in this study. In the paper a kind of T-S fuzzy neural network controller is used ... Keywords: Dynamic positioning, Semisubmersible drilling platform, T-S fuzzy neural network

Yan Li; Yu Gu

2012-10-01T23:59:59.000Z

283

BLACK-BOX MODELLING OF HVAC SYSTEM: IMPROVING THE PERFORMANCES OF NEURAL NETWORKS  

E-Print Network [OSTI]

BLACK-BOX MODELLING OF HVAC SYSTEM: IMPROVING THE PERFORMANCES OF NEURAL NETWORKS Eric FOCK Ile de La Réunion - FRANCE ABSTRACT This paper deals with neural networks modelling of HVAC systems of HVAC system can be modelled using manufacturer design data presented as derived performance maps

Boyer, Edmond

284

Letters: Neural network based hybrid computing model for wind speed prediction  

Science Journals Connector (OSTI)

This paper proposes a Neural Network based hybrid computing model for wind speed prediction in renewable energy systems. Wind energy is one of the renewable energy sources which lower the cost of electricity production. Due to the fluctuation and nonlinearity ... Keywords: Hybrid Model, Multilayer Perceptron, Neural Networks, Self Organizing Maps, Wind Speed Prediction

K. Gnana Sheela; S. N. Deepa

2013-12-01T23:59:59.000Z

285

QUANTITATIVE EVALUATION OF NEURAL NETWORKS FOR NDE APPLICATIONS USING THE ROC CURVE  

E-Print Network [OSTI]

1 QUANTITATIVE EVALUATION OF NEURAL NETWORKS FOR NDE APPLICATIONS USING THE ROC CURVE Mackay A. E waveform identification in NDE equipment, and to compare neural network performance with other methods. NDE-based scheme in classifying real-world eddy current data collected from an aircraft wheel NDE system. KEY WORDS

MacIver, Malcolm A.

286

Periodically Intermittent Stabilization of Delayed Neural Networks Based on Piecewise Lyapunov Functions/Functionals  

Science Journals Connector (OSTI)

This paper is concerned with the stabilization problem of delayed neural networks via a periodically intermittent controller. Two cases of time-varying bounded delays are considered: one is the time-varying delay without any constraints on the delay ... Keywords: Delayed neural networks, Intermittent control, Linear matrix inequalities, Piecewise Lyapunov functions/functionals

Wu-Hua Chen, Jiacheng Zhong, Zhiyong Jiang, Xiaomei Lu

2014-12-01T23:59:59.000Z

287

Stability criteria for BAM neural networks with leakage delays and probabilistic time-varying delays  

Science Journals Connector (OSTI)

This paper is concerned with the stability criteria for bidirectional associative memory (BAM) neural networks with leakage time delay and probabilistic time-varying delays. By establishing a stochastic variable with Bernoulli distribution, the information ... Keywords: BAM neural networks, Leakage delay, Probabilistic time-varying delays

S. Lakshmanan, Ju H. Park, Tae H. Lee, H. Y. Jung, R. Rakkiyappan

2013-05-01T23:59:59.000Z

288

Neural Network Based Montioring and Control of Fluidized Bed.  

SciTech Connect (OSTI)

The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to the experimental system. In this report, the hardware implementation issues of the control algorithms are also discussed.

Bodruzzaman, M.; Essawy, M.A.

1996-04-01T23:59:59.000Z

289

Journal of the American College of Cardiology 1996;28:10121016 Agreement between Artificial Neural Networks and Human Expert  

E-Print Network [OSTI]

Artificial Neural Networks and Human Expert for the Electrocardiographic Diagnosis of Healed Myocardial Infarction Hedén; Artificial Neural Network and Human Expert Bo Hedén, MD 1 , Mattias Ohlsson, PhD 2 , Ralf artificial neural networks and an experienced electrocardiographer diagnosing healed myocardial infarction

Peterson, Carsten

290

How might an artificial neural network represent metric space? Patricia M.Boechler and Michael R.W.Dawson  

E-Print Network [OSTI]

How might an artificial neural network represent metric space? Patricia M.Boechler and Michael R.W.Dawson Department of Psychology,University of Alberta CONCLUS IONS Although this artificial neural network. + + + + + + + ABS TR ACT An artificial neural network was trained to rate the distances between pairs of cities

Dawson, Michael

291

Artificial Neural Network Model for fMRI timeseries and a Framework for Comparison of Convolution Models  

E-Print Network [OSTI]

Artificial Neural Network Model for fMRI timeseries and a Framework for Comparison of Convolution of the hemodynamic response was proposed in [5]. Here we propose an artificial neural network (ANN) model. Method The specific neural network we will use is a two­layer feed­forward type. Parameters are optimized

Nielsen, Finn Ã?rup

292

SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network  

E-Print Network [OSTI]

of an artificial neural network Yang Shen · Ad Bax Received: 4 June 2010 / Accepted: 30 June 2010 / Published a new chemical shift prediction program, SPARTA?, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high

Bax, Ad

293

Reprinted from: Artificial Neural Networks for Speech and Vision (Proc. Workshop held at Rutgers, 1992, Edited by Richard J. Mammone)  

E-Print Network [OSTI]

Reprinted from: Artificial Neural Networks for Speech and Vision (Proc. Workshop held at Rutgers-125 UNIQUENESS OF WEIGHTS FOR NEURAL NETWORKS Francesca Albertini and Eduardo D. Sontag Department of Mathematics by neural networks have very little redundancy.) In this short expository survey, we sketch various known

Sontag, Eduardo

294

Artificial Neural Networks as a Tool for Galaxy Classification  

E-Print Network [OSTI]

We describe an Artificial Neural Network (ANN) approach to classification of galaxy images and spectra. ANNs can replicate the classification of galaxy images by a human expert to the same degree of agreement as that between two human experts, to within 2 T-type units. Similar methods are applied to classification of galaxy spectra. In particular, Principal Component Analysis of galaxy spectra can be used to compress the data, to suppress noise and to provide input to the ANNs. These and other classification methods will soon be applied to the Anglo-Australian 2-degree-Field (2dF) redshift survey of 250,000 galaxies.

Ofer Lahav

1996-12-10T23:59:59.000Z

295

Neural network and area method interpretation of pulsed experiments  

SciTech Connect (OSTI)

The determination of the subcriticality level is an important issue in accelerator-driven system technology. The area method, originally introduced by N. G. Sjoestrand, is a classical technique to interpret flux measurement for pulsed experiments in order to reconstruct the reactivity value. In recent times other methods have also been developed, to account for spatial and spectral effects, which were not included in the area method, since it is based on the point kinetic model. The artificial neural network approach can be an efficient technique to infer reactivities from pulsed experiments. In the present work, some comparisons between the two methods are carried out and discussed. (authors)

Dulla, S.; Picca, P.; Ravetto, P. [Politecnico di Torino, Dipartimento di Energetica, Corso Duca degli Abruzzi, 24 - 10129 Torino (Italy); Canepa, S. [Lab of Reactor Physics and Systems Behaviour LRS, Paul Scherrer Inst., 5232 Villigen (Switzerland)

2012-07-01T23:59:59.000Z

296

Improving Combinatorial Ambiguities of ttbar Events Using Neural Networks  

E-Print Network [OSTI]

We present a method for resolving the combinatorial issues in the \\ttbar lepton+jets events occurring at the Tevatron collider. By incorporating multiple information into an artificial neural network, we introduce a novel event reconstruction method for such events. We find that this method significantly reduces the number of combinatorial ambiguities. Compared to the classical reconstruction method, our method provides significantly higher purity with same efficiency. We illustrate the reconstructed observables for the realistic top-quark mass and the forward-backward asymmetry measurements. A Monte Carlo study shows that our method provides meaningful improvements in the top-quark measurements using same amount of data as other methods.

Ji Hyun Shim; Hyun Su Lee

2014-02-17T23:59:59.000Z

297

Artificial neural network: an upcoming strategic decision planning support tool  

Science Journals Connector (OSTI)

Finding a reliable technique to help in the strategic planning is not a new research issue. Past studies, using several techniques and models, have shown only limited progress. Existing techniques are either too complex and time-consuming or too unreliable for full-scale adoption. Artificial neural network (ANN) technology seems capable of overcoming most of these shortcomings. This paper overviews existing techniques: the strategic-fit concept, CAPM, traditional DSS, and expert systems and contrasts them with ANN. An illustrative application is used to demonstrate the effectiveness of ANN in supporting a diversification strategic planning.

Godwin J. Udo

1998-01-01T23:59:59.000Z

298

An artificial neural network application on nuclear charge radii  

E-Print Network [OSTI]

The artificial neural networks (ANNs) have emerged with successful applications in nuclear physics as well as in many fields of science in recent years. In this paper, by using (ANNs), we have constructed a formula for the nuclear charge radii. Statistical modeling of nuclear charge radii by using ANNs has been seen as to be successful. Also, the charge radii, binding energies and two-neutron separation energies of Sn isotopes have been calculated by implementing of the new formula in Hartree-Fock-Bogoliubov (HFB) calculations. The results of the study shows that the new formula is useful for describing nuclear charge radii.

S. Akkoyun; T. Bayram; S. O. Kara; A. Sinan

2012-12-27T23:59:59.000Z

299

Analysis of natural gradient descent for multilayer neural networks  

Science Journals Connector (OSTI)

Natural gradient descent is a principled method for adapting the parameters of a statistical model on-line using an underlying Riemannian parameter space to redefine the direction of steepest descent. The algorithm is examined via methods of statistical physics that accurately characterize both transient and asymptotic behavior. A solution of the learning dynamics is obtained for the case of multilayer neural network training in the limit of large input dimension. We find that natural gradient learning leads to optimal asymptotic performance and outperforms gradient descent in the transient, significantly shortening or even removing plateaus in the transient generalization performance that typically hamper gradient descent training.

Magnus Rattray and David Saad

1999-04-01T23:59:59.000Z

300

Artificial neural network based on SQUIDs: demonstration of network training and operation  

Science Journals Connector (OSTI)

We propose a scheme for the realization of artificial neural networks based on superconducting quantum interference devices (SQUIDs). In order to demonstrate the operation of this scheme we designed and successfully tested a small network that implements an XOR gate and is trained by means of examples. The proposed scheme can be particularly convenient as support for superconducting applications such as detectors for astrophysics, high energy experiments, medicine imaging and so on.

F Chiarello; P Carelli; M G Castellano; G Torrioli

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

Nonlinear adaptive internal model control using neural networks  

E-Print Network [OSTI]

]. This concept of relative order allows one to determine whether the mapping between the input and out, put is one to one. For our system with Relative Order I, Lsh(*) j 0 Now, ah Lth(s) = ? f(*) as 3 191 W W as ah Lgh(s) = ? g(s) r ays Ws ? Wg rig 3...(. ) are the same asin the original system and L&h(Z) and LgL& 'h(Z) are Lze derivatives. rz is the relative order of the system. Thus the inverse of the recurrent neural network is the network itself. For cz = 1, ~s, ? Lgh(Z) Lsh(Z) Lsh(Z) g 0 m Ws ? Wzziz...

Gandhi, Amit Krushnavadan

2012-06-07T23:59:59.000Z

302

Smart Home Design for Disabled People based on Neural Networks  

Science Journals Connector (OSTI)

Abstract Toward facilitating the everyday life of disabled people researches have put together technologies such as computing, networking and telecommunication in one environment called a Smart Home. Enabling disabled people to overcome their handicap by providing a system that replaces what they lack is what makes such a work interesting and important. We have developed such a space by stepping ahead of past researches and not only reaching a preprogrammed automatic home, but also a learning and self-adapting intelligent home. This was accomplished by integrating two types of neural networks to our system. To show the effectiveness of the system, we developed a first prototype that covers parts of the theoretical design. Further work can be done by actually transforming our prototype to an actual house where disabled people may benefit from.

Ali Hussein; Mehdi Adda; Mirna Atieh; Walid Fahs

2014-01-01T23:59:59.000Z

303

An Artificial Neural Network Approach to Classification of Galaxy Spectra  

E-Print Network [OSTI]

We present a method for automated classification of galaxies with low signal-to-noise (S/N) spectra typical of redshift surveys. We develop spectral simulations based on the parameters for the 2dF Galaxy Redshift Survey, and with these simulations we investigate the technique of Principal Component Analysis when applied specifically to spectra of low S/N. We relate the objective principal components to features in the spectra and use a small number of components to successfully reconstruct the underlying signal from the low quality spectra. Using the principal components as input, we train an Artificial Neural Network to classify the noisy simulated spectra into morphological classes, revealing the success of the classification against the observed $b_{\\rm J}$ magnitude of the source, which we compare with alternative methods of classification. We find that more than 90\\% of our sample of normal galaxies are correctly classified into one of five broad morphological classes for simulations at $b_{\\rm J}$=19.7. By dividing the data into separate sets we show that a classification onto the Hubble sequence is only relevant for normal galaxies and that spectra with unusual features should be incorporated into a classification scheme based predominantly on their spectral signatures. We discuss how an Artificial Neural Network can be used to distinguish normal and unusual galaxy spectra, and discuss the possible application of these results to spectra from galaxy redshift surveys.

S. R. Folkes; O. Lahav; S. J. Maddox

1996-08-13T23:59:59.000Z

304

Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics  

Science Journals Connector (OSTI)

Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%.

L Lenhardt; I Zekovi?; T Drami?anin; M D Drami?anin

2013-01-01T23:59:59.000Z

305

Parallel distributed processing models using the back-propagation rule for studying analytic and holistic modes of processing in category learning  

E-Print Network [OSTI]

PARALLEL DISTRIBUTED PROCESSING MODELS USING THE BACK-PROPAGATION RULE FOR STUDYING ANALYTIC AND HOLISTIC MODES OF PROCESSING IN CATEGORY LEARNING A Thesis NIELS KONRAD BAUER Submitted to the Graduate College of Texas A&M University... IN CATEGORY LEARNING A Thesis NIELS KONRAD BAUER Appro d as to style and content by: S enM. Mo an (C air of Committee) 7 Thomas B. Ward (Member) Amitabha Mukerj ee (Member) Glenn N. Williams (Head of Department) May 1988 ABSTRACT Parallel...

Bauer, Niels Konrad

1988-01-01T23:59:59.000Z

306

Tuning the stator resistance of induction motors using artificial neural network  

SciTech Connect (OSTI)

Tuning the stator resistance of induction motors is very important, especially when it is used to implement direct torque control (DTC) in which the stator resistance is a main parameter. In this paper, an artificial network (ANN) is used to accomplish tuning of the stator resistance of an induction motor. The parallel recursive prediction error and backpropagation training algorithms were used in training the neural network for the simulation and experimental results, respectively. The neural network used to tune the stator resistance was trained on-line, making the DTC strategy more robust and accurate. Simulation results are presented for three different neural-network configurations showing the efficiency of the tuning process. Experimental results were obtained for the one of the three neural-network configuration. Both simulation and experimental results showed that the ANN have tuned the stator resistance in the controller to track actual resistance of the machine.

Cabrera, L.A.; Elbuluk, M.E.; Husain, I. [Univ. of Akron, OH (United States). Dept. of Electrical Engineering] [Univ. of Akron, OH (United States). Dept. of Electrical Engineering

1997-09-01T23:59:59.000Z

307

Artificial Neural Network Applied to Prediction of Fluorquinolone Antibacterial Activity by Topological Methods  

Science Journals Connector (OSTI)

This method uses neural networks in which training algorithms are used as well as different concepts and methods of artificial intelligence with a suitable set of topological descriptors. ... The artificial neural network seeks to capitalize upon this design by providing a network of neurons, arranged in layers, which all contribute in some measure to complex decisions. ... In the typical neural net every connection between two neurons is associated with a weight, a positive or negative real number which multiplies the signal from the preceding neuron. ...

José Jaén-Oltra; Ma Teresa Salabert-Salvador; Francisco J. García-March; Facundo Pérez-Giménez; Francisco Tomás-Vert

2000-02-24T23:59:59.000Z

308

Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill  

SciTech Connect (OSTI)

The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved.

Moussaoui, A. K. [Electrical Engineering Laboratory of Guelma (LGEG), BP.401, University of Guelma, 24000 (Algeria); Abbassi, H. A.; Bouazza, S. [Universite Badji Mokhtar BP 12--23000-Annaba Algerie (Algeria)

2008-06-12T23:59:59.000Z

309

Application of artificial neural networks for damage indices classification with the use of Lamb waves for the aerospace structures.  

E-Print Network [OSTI]

Application of artificial neural networks for damage indices classification with the use of Lamb of view. Artificial neural network has been used for the classification of fatigue cracks and artificial@agh.edu.pl, *corresponding author Keywords: NDT, Ultrasonic testing, Lamb waves, Artificial intelligence, Artificial Neural

310

An artificial neural network application on nuclear charge radii  

Science Journals Connector (OSTI)

Artificial neural networks (ANN) have emerged with successful applications in nuclear physics as well as in many fields of science in recent years. In this paper, ANN have been employed on experimental nuclear charge radii. Statistical modeling of nuclear charge radii using ANN are seen to be successful. Based on the outputs of ANN we have estimated a new simple mass-dependent nuclear charge radii formula. Also, the charge radii, binding energies and two-neutron separation energies of Sn isotopes have been calculated by implementation of a new estimated formula in Hartree–Fock–Bogoliubov calculations. The results of the study show that the new estimated formula is useful for describing nuclear charge radii.

S Akkoyun; T Bayram; S O Kara; A Sinan

2013-01-01T23:59:59.000Z

311

ANNz: estimating photometric redshifts using artificial neural networks  

E-Print Network [OSTI]

We introduce ANNz, a freely available software package for photometric redshift estimation using Artificial Neural Networks. ANNz learns the relation between photometry and redshift from an appropriate training set of galaxies for which the redshift is already known. Where a large and representative training set is available ANNz is a highly competitive tool when compared with traditional template-fitting methods. The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release 1, and for this particular data set the r.m.s. redshift error in the range 0 < z < 0.7 is 0.023. Non-ideal conditions (spectroscopic sets which are small, or which are brighter than the photometric set for which redshifts are required) are simulated and the impact on the photometric redshift accuracy assessed.

Adrian A. Collister; Ofer Lahav

2003-11-03T23:59:59.000Z

312

Galaxy Classification by Human Eyes and by Artificial Neural Networks  

E-Print Network [OSTI]

The rapid increase in data on galaxy images at low and high redshift calls for re-examination of the classification schemes and for new automatic objective methods. Here we present a classification method by Artificial Neural Networks. We also show results from a comparative study we carried out using a new sample of 830 APM digitised galaxy images. These galaxy images were classified by 6 experts independently. It is shown that the ANNs can replicate the classification by a human expert almost to the same degree of agreement as that between two human experts, to within 2 $T$-type units. Similar methods can be applied to automatic classification of galaxy spectra. We illustrate it by Principal Component Analysis of galaxy spectra, and discuss future large surveys.

Ofer Lahav

1995-05-19T23:59:59.000Z

313

APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION  

SciTech Connect (OSTI)

Stripline BPM sensors contain inherent non-linearities, as a result of field distortions from the pickup elements. Many methods have been devised to facilitate corrections, often employing polynomial fitting. The cost of computation makes real-time correction difficult, particulalry when integer math is utilized. The application of neural-network technology, particularly the multi-layer perceptron algorithm, is proposed as an efficient alternative for electrode linearization. A process of supervised learning is initially used to determine the weighting coefficients, which are subsequently applied to the incoming electrode data. A non-linear layer, known as an ?activation layer,? is responsible for the removal of saturation effects. Implementation of a perceptron in an FPGA-based software-defined radio (SDR) is presented, along with performance comparisons. In addition, efficient calculation of the sigmoidal activation function via the CORDIC algorithm is presented.

Musson, John C. [JLAB; Seaton, Chad [JLAB; Spata, Mike F. [JLAB; Yan, Jianxun [JLAB

2012-11-01T23:59:59.000Z

314

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 4, JULY 1997 1 Neural Intelligent Control for a Steel Plant  

E-Print Network [OSTI]

for a Steel Plant G´erard Bloch, Frank Sirou, Vincent Eustache, and Philippe Fatrez Abstract--The improvement--Intelligent control, fault diagnosis, galvanneal- ing, neural network, modeling, steel industry. I. INTRODUCTION Worldwide use of metallic coated steel sheet in the car industry is continually increasing. Particularly

Paris-Sud XI, Université de

315

Knowledge Extraction from Neural Networks using the All-Permutations Fuzzy Rule Base  

E-Print Network [OSTI]

Knowledge Extraction from Neural Networks using the All-Permutations Fuzzy Rule Base Eyal Kolman and a specific fuzzy rule base to extract the knowledge embedded in the network. We demonstrate this using and comprehensible description of the knowledge learned by the network during its training. Keywords: Feedforward

Margaliot, Michael

316

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks Hydrology and Earth System Sciences, 6(4), 641654 (2002) EGS  

E-Print Network [OSTI]

streamflow generation using a hybrid model based on artificial neural networks J.C. Ochoa-Rivera, R. GarcíaMultivariate synthetic streamflow generation using a hybrid model based on artificial neural neural network. The structure of the model results from two components, the neural network (NN

Paris-Sud XI, Université de

317

A New Improved Na-K Geothermometer By Artificial Neural Networks | Open  

Open Energy Info (EERE)

Improved Na-K Geothermometer By Artificial Neural Networks Improved Na-K Geothermometer By Artificial Neural Networks Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Journal Article: A New Improved Na-K Geothermometer By Artificial Neural Networks Details Activities (0) Areas (0) Regions (0) Abstract: A new Na/K geothermometer equation has been developed. The temperature function is:Concentrations are in mg/kg. The new improved geothermometer equation was developed by artificial neural networks. The normalized mean square error (NMSE) used in the new improved Na/K equation for temperatures ranging from 94 to 345°C is 0.179, which is lower than the corresponding NMSE 0.226, 0.598, 0.656, 0.268, 0.328 and 0.225 for the equations of Arnorsson et al. (1983; Geochim. Cosmochim. Acta 47, 567-577), Truesdell (1975; Proc. 2nd UN Symposium), Tonani (1980; Proc. Adv. Eur.

318

Artificial Neural Networks and Long-Range Precipitation Prediction in California  

Science Journals Connector (OSTI)

Artificial neural networks (ANNs), which are modeled on the operating behavior of the brain, are tolerant of some imprecision and are especially useful for classification and function approximation/mapping problems, to which hard and fast rules ...

David Silverman; John A. Dracup

2000-01-01T23:59:59.000Z

319

Artificial Neural Network Model for the Gap Discontinuity in Shielded Coplanar Waveguide  

Science Journals Connector (OSTI)

Fast and accurate component models are essential for the development of three dimensional millimeter wave integrated circuits. In this paper, an accurate and efficient artificial neural network (ANN) model for th...

Xiaozheng Zhong; Bing-Zhong Wang…

2001-08-01T23:59:59.000Z

320

Artificial Neural Network Models for the Double-Vias in Multilayer Stripline Circuits  

Science Journals Connector (OSTI)

Multi-vias is a kind of interconnection largely existing in the multi-chip module (MCM) packages for high-speed digital circuits. In this paper, a multilayer perceptron neural network (MLPNN) is used to model ...

Bing-Zhong Wang; Shaoyun Zou

1999-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

Development and implementation of an artificially intelligent search algorithm for sensor fault detection using neural networks  

E-Print Network [OSTI]

This work is aimed towards the development of an artificially intelligent search algorithm used in conjunction with an Auto Associative Neural Network (AANN) to help locate and reconstruct faulty sensor inputs in control systems. The AANN can...

Singh, Harkirat

2004-09-30T23:59:59.000Z

322

Artificial Neural Networks for Modelling and Control of Non-Linear Systems  

Science Journals Connector (OSTI)

From the Publisher:Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are the universal approximation ability, the ...

Johan A. Suykens; J. Vandewalle; Bart L. R. de Moor

1996-01-01T23:59:59.000Z

323

An Artificial Neural Network Approach to Multispectral Rainfall Estimation over Africa  

Science Journals Connector (OSTI)

Multispectral Spinning Enhanced Visible and IR Interferometer (SEVIRI) data, calibrated with daily rain gauge estimates, were used to produce daily high-resolution rainfall estimates over Africa. An artificial neural network (ANN) approach was ...

Robin Chadwick; David Grimes

2012-06-01T23:59:59.000Z

324

E-Print Network 3.0 - associative memory neural Sample Search...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

0521642981 You can buy this book for 30 pounds or 50. See http:www.inference.phy.cam.ac.ukmackayitila for links. Summary: real problems with neural networks, please...

325

Hybridizing Exponential Smoothing and Neural Network for Financial Time Series Predication  

Science Journals Connector (OSTI)

In this study, a hybrid synergy model integrating exponential smoothing and neural network is proposed for financial ... attempts to incorporate the linear characteristics of an exponential smoothing model and no...

Kin Keung Lai; Lean Yu; Shouyang Wang; Wei Huang

2006-01-01T23:59:59.000Z

326

Gaussian radial basis function neural network controller of a synchronous reluctance motor in electric motorcycle applications  

Science Journals Connector (OSTI)

In this article, a sliding mode control (SMC) design based on a Gaussian radial basis function neural network (GRBFNN) is proposed for the synchronous reluctance motor (SynRM) system in electrical motorcycle a...

Chien-An Chen; Huann-Keng Chiang; Wen-Bin Lin

2010-12-01T23:59:59.000Z

327

Application of fuzzy SOFM neural network and rough set theory on fault diagnosis for rotating machinery  

Science Journals Connector (OSTI)

This paper presents a new method that applies fuzzy logic, rough set theory and SOFM neural network to rotating machinery fault diagnosis. In this method, firstly, relationships between the fault causations and fault symptoms are established by fuzzy ...

Dongxiang Jiang; Kai Li; Gang Zhao; Jinhui Diao

2005-05-01T23:59:59.000Z

328

Mining customer credit by using neural network model with logistic regression approach  

E-Print Network [OSTI]

. The objective of this research was to investigate the methodologies to mine customer credit history for the bank industry. Particularly, combination of logistic regression model and neural network technique are proposed to determine if the predictive capability...

Kao, Ling-Jing

2001-01-01T23:59:59.000Z

329

Application of Radial Basis Function Neural Network in Modeling Wastewater Sludge Recycle System  

Science Journals Connector (OSTI)

Sludge recycle system is an important part of wastewater treatment plants(WWTP), which can ensure ... Neural Network model for prediction of the Sludge recycling flowrate, which ultimately affect the Sludge recycling

Long Luo; Liyou Zhou

2010-01-01T23:59:59.000Z

330

NEURAL NETWORKS FOR DISCRETE TOMOGRAPHY K.J. Batenburg a W.A. Kosters b  

E-Print Network [OSTI]

NEURAL NETWORKS FOR DISCRETE TOMOGRAPHY K.J. Batenburg a W.A. Kosters b a Mathematical Institute of crystalline solids at atomic resolution from electron microscopic images can be considered the ``holy grail

Kosters, Walter

331

Optimization of the deflection basin by genetic algorithm and neural network approach  

Science Journals Connector (OSTI)

This paper introduces a new concept of integrating artificial neural networks (ANN) and genetic algorithms (GA) in modeling the deflection basins measured on the flexible pavements. Backcalculating pavement layer moduli are well-accepted procedures for ...

Serdal Terzi; Mehmet Saltan; Tulay Yildirim

2003-06-01T23:59:59.000Z

332

Prediction of ground subsidence in Samcheok City, Korea using artificial neural networks and GIS  

Science Journals Connector (OSTI)

This study shows the construction of a hazard map for presumptive ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in ... neural network, with a geographic information system (GIS

Ki-Dong Kim; Saro Lee; Hyun-Joo Oh

2009-07-01T23:59:59.000Z

333

Optoelectronic implementations of Pulse-Coupled Neural Networks : challenges and limitations  

E-Print Network [OSTI]

This thesis examines Pulse Coupled Neural Networks (PCNNs) and their applications, and the feasibility of a compact, rugged, cost-efficient optoelectronic implementation. Simulation results are presented. Proposed optical ...

Wise, Raydiance (Raydiance Raychele)

2007-01-01T23:59:59.000Z

334

HotStrength of Ferritic CreepResistant Steels Comparison of Neural Network and Genetic Programming  

E-Print Network [OSTI]

on the development of materials. In our continuing research on steels for the energy production industries [3], we that methods such as neural networks, genetic programming and optimisation techniques have made a mark

Cambridge, University of

335

A neural network optimization-based method of image reconstruction from projections  

Science Journals Connector (OSTI)

The image reconstruction from projections problem remains the primary concern for scientists in area of computer tomography. The presented paper describes a new approach to the reconstruction problem using a recurrent neural network. The reconstruction ...

Robert Cierniak

2010-03-01T23:59:59.000Z

336

Neural Network Technology as a Pollution Prevention Tool in the Electric Utility Industry  

E-Print Network [OSTI]

This paper documents efforts by the Lower Colorado River Authority (LCRA) to pilot test the use of neural network technology as a pollution prevention tool for reducing stack emissions from a natural gas-fired power generating facility. The project...

Johnson, M. L.

337

Artificial Neural Network Meta Models To Enhance the Prediction and Consistency of Multiphase Reactor Correlations  

Science Journals Connector (OSTI)

Artificial Neural Network Meta Models To Enhance the Prediction and Consistency of Multiphase Reactor Correlations ... Artificial neural networks (ANNs), as correlation tools, have gained wide acceptance in the field because of their inherent ability to map nonlinear relationships that tie up independent variables (either as dimensional inputs, e.g., pressure, diameter, etc., or as dimensionless inputs, e.g., Reynolds, Weber, and Froude numbers, etc.) to the reactor characteristics to be predicted, i.e., dimensional or dimensionless output. ...

Laurentiu A. Tarca; Bernard P. A. Grandjean; Faïçal Larachi

2003-03-19T23:59:59.000Z

338

A hierarchical structure of neural network implemented for the recognition of automobile license plate number  

E-Print Network [OSTI]

A HIERARCHICAL STRUCTURE OF NEURAL NETWORK IMPLEMENTED FOR THE RECOGNITION OF AUTOMOBILE LICENSE PLATE NURSER A Thesis by JOONGHO CHANG Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfdlment... of the requirements for the degree of MASTER OF SCIENCE August 1994 Major Subject: Electrical Engineering A HIERARCHICAL STRUCTURE OF NEURAL NETWORK IMPLEMENTED FOR THE RECOGNITION OF AUTOMOBILE LICENSE PLATE NUMBER A Thesis by JOONGHO CHANG Submitted...

Chang, Joongho

1994-01-01T23:59:59.000Z

339

Application of neural networking in live cattle futures market: an approach to price-forecasting  

E-Print Network [OSTI]

-Ju Chou, B. S. , Tunghai University, Taiwan Chair of Advisory Committee Dr. John P. Walter The ability to forecast closing price changes using neural networking technique in the live cattle futures market was investigated. Futures prices and contract... volumes from 1977 through 1991 were obtained for four commodities: live cattle, feeder cattle, live hogs and corn. Twelve neural networks were constructed, one for each combination of six contract months and two uading periods. The two trading periods...

Chou, Chien-Ju

2012-06-07T23:59:59.000Z

340

Neural network based approach for tuning of SNS feedback and feedforward controllers.  

SciTech Connect (OSTI)

The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

Kwon, S. I. (Sung-Il); Prokop, M. S. (Mark S.); Regan, A. H. (Amy H.)

2002-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Flaws Identification Using Eddy Current Differential Transducer and Artificial Neural Networks  

SciTech Connect (OSTI)

In this paper we present a multi-frequency excitation eddy current differential transducer and dynamic neural models which were used to detect and identify artificial flaws in thin conducting plates. Plates are made of Inconel600. EDM notches have relative depth from 10% to 80% and length from 2 mm to 7 mm. All flaws were located on the opposite surface of the examined specimen. Measured signals were used as input for training and verifying dynamic neural networks with a moving window. Wide range of ANN (Artificial Neural Network) structures are examined for different window length and different number of frequency components in excitation signal. Observed trends are presented in this paper.

Chady, T.; Lopato, P. [Szczecin University of Technology. al Piastow 17, 70-310 Szczecin (Poland)

2006-03-06T23:59:59.000Z

342

Reprinted from: Artificial Neural Networks for Speech and Vision (Proc. Workshop held at Rutgers, 1992, Edited by Richard J. Mammone)  

E-Print Network [OSTI]

Reprinted from: Artificial Neural Networks for Speech and Vision (Proc. Workshop held at Rutgers­125 UNIQUENESS OF WEIGHTS FOR NEURAL NETWORKS Francesca Albertini \\Lambda and Eduardo D. Sontag y Department into a network, in order to increase representational bias, by imposing artificial conditions such as asking

Sontag, Eduardo

343

Mind Uploading: Artificial neural network, Artificial intelligence, Neuroinformatics, Computational neuroscience, Transhumanism, Douglas Hofstadter, Jeff ... Mind transfer in fiction  

Science Journals Connector (OSTI)

Mind uploading - Artificial neural network, Artificial intelligence, Neuroinformatics, Computational neuroscience, Transhumanism, Douglas Hofstadter, Jeff Hawkins, Marvin Minsky, Rodolfo Llins, Moore's law, Magnetoencephalography, Mind transfer in fiction, ...

John McBrewster; Frederic P. Miller; Agnes F. Vandome

2009-04-01T23:59:59.000Z

344

An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs)  

Science Journals Connector (OSTI)

Abstract Precise and consistent production forecasting is indeed an important step for the management and planning of petroleum reservoirs. A new neural approach to forecast cumulative oil production using higher-order neural network (HONN) has been applied in this study. HONN overcomes the limitation of the conventional neural networks by representing linear and nonlinear correlations of neural input variables. Thus, HONN possesses a great potential in forecasting petroleum reservoir productions without sufficient training data. Simulation studies were carried out on a sandstone reservoir located in Cambay basin in Gujarat, India, to prove the efficacy of \\{HONNs\\} in forecasting cumulative oil production of the field with insufficient field data available. A pre-processing procedure was employed in order to reduce measurement noise in the production data from the oil field by using a low pass filter and optimal input variable selection using cross-correlation function (CCF). The results of these simulation studies indicate that the HONN models have good forecasting capability with high accuracy to predict cumulative oil production.

N. Chithra Chakra; Ki-Young Song; Madan M. Gupta; Deoki N. Saraf

2013-01-01T23:59:59.000Z

345

Automatic Detection of Expanding HI Shells Using Artificial Neural Networks  

E-Print Network [OSTI]

The identification of expanding HI shells is difficult because of their variable morphological characteristics. The detection of HI bubbles on a global scale therefore never has been attempted. In this paper, an automatic detector for expanding HI shells is presented. The detection is based on the more stable dynamical characteristics of expanding shells and is performed in two stages. The first one is the recognition of the dynamical signature of an expanding bubble in the velocity spectra, based on the classification of an artificial neural network. The pixels associated with these recognized spectra are identified on each velocity channel. The second stage consists in looking for concentrations of those pixels that were firstly pointed out, and to decide if they are potential detections by morphological and 21-cm emission variation considerations. Two test bubbles are correctly detected and a potentially new case of shell that is visually very convincing is discovered. About 0.6% of the surveyed pixels are identified as part of a bubble. These may be false detections, but still constitute regions of space with high probability of finding an expanding shell. The subsequent search field is thus significantly reduced. We intend to conduct in the near future a large scale HI shells detection over the Perseus Arm using our detector.

Anik Daigle; Gilles Joncas; Marc Parizeau; Marc-Antoine Miville-Deschenes

2003-04-17T23:59:59.000Z

346

Artificial neural network application for modeling the rail rolling process  

Science Journals Connector (OSTI)

Abstract Rail rolling process is one of the most complicated hot rolling processes. Evaluating the effects of parametric values on this complex process is only possible through modeling. In this study, the production parameters of different types of rails in the rail rolling processes were modeled with an artificial neural network (ANN), and it was aimed to obtain optimum parameter values for a different type of rail. For this purpose, the data from the Rail and Profile Rolling Mill in Kardemir Iron & Steel Works Co. (Karabük, Turkey) were used. BD1, BD2, and Tandem are three main parts of the rolling mill, and in order to obtain the force values of the 49 kg/m rail in each pass for the BD1 and BD2 sections, the force and torque values for the Tandem section, parameter values of 60, 54, 46, and 33 kg/m type rails were used. Comparing the results obtained from the ANN model and the actual field data demonstrated that force and torque values were obtained with acceptable error rates. The results of the present study demonstrated that ANN is an effective and reliable method to acquire data required for producing a new rail, and concerning the rail production process, it provides a productive way for accurate and fast decision making.

Hüseyin Alt?nkaya; ?lhami M. Orak; ?smail Esen

2014-01-01T23:59:59.000Z

347

Multi-parameter estimating photometric redshifts with artificial neural networks  

E-Print Network [OSTI]

We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 Galaxy Sample using artificial neural networks (ANNs). Different input patterns based on various parameters (e.g. magnitude, color index, flux information) are explored and their performances for redshift prediction are compared. For ANN technique, any parameter may be easily incorporated as input, but our results indicate that using dereddening magnitude produces photometric redshift accuracies often better than the Petrosian magnitude or model magnitude. Similarly, the model magnitude is also superior to Petrosian magnitude. In addition, ANNs also show better performance when the more effective parameters increase in the training set. Finally, the method is tested on a sample of 79, 346 galaxies from the SDSS DR2. When using 19 parameters based on the dereddening magnitude, the rms error in redshift estimation is sigma(z)=0.020184. The ANN is highly competitive tool when compared with traditional template-fitting methods where a large and representative training set is available.

Lili Li; Yanxia Zhang; Yongheng Zhao; Dawei Yang

2006-12-28T23:59:59.000Z

348

A neural network analysis of concert hall acoustics  

Science Journals Connector (OSTI)

A neural network analysis using data from 51 concert halls was undertaken. The analysis related the acoustic quality of halls as judged by musicians to ten hall parameters: volume surface area number of seats length width height mean rake angle of seats a surface diffusion index stage height and extent of stage shell/enclosure. The surface diffusion index and the extent of the stage shell were determined by a group of architects who made judgments on the basis of photographs and drawings of the halls. The results of the analysis are tentative and difficult to generalize as there are so many inputs and so many possible combinations of parameters and the effect of a particular factor is in some cases neither linear nor monotonic. The results are applied to a particular hall the concert hall of the Sydney Opera House where changes are being contemplated. There are some unexpected results which at this stage give food for thought rather than the basis for action as there are obvious limitations to this work such as extraneous factors influencing judgments of acoustic quality surface diffusion estimation and extent of the stage shell and the limited number inputs used to describe the halls. ?

Fergus R. Fricke; Young G. Han

1998-01-01T23:59:59.000Z

349

Hopfield Neural Network deconvolution for weak lensing measurement  

E-Print Network [OSTI]

Weak gravitational lensing has the potential to place tight constraints on the equation of the state of dark energy. However, this will only be possible if shear measurement methods can reach the required level of accuracy. We present a new method to measure the ellipticity of galaxies used in weak lensing surveys. The method makes use of direct deconvolution of the data by the total Point Spread Function (PSF). We adopt a linear algebra formalism that represents the PSF as a Toeplitz matrix. This allows us to solve the convolution equation by applying the Hopfield Neural Network iterative scheme. The ellipticity of galaxies in the deconvolved images are then measured using second order moments of the autocorrelation function of the images. To our knowledge, it is the first time full image deconvolution is used to measure weak lensing shear. We apply our method to the simulated weak lensing data proposed in the GREAT10 challenge and obtain a quality factor of Q=87. This result is obtained after applying image...

Nurbaeva, Guldariya; Courbin, Frederic; Meylan, Georges

2014-01-01T23:59:59.000Z

350

A neural network based model for urban noise prediction  

Science Journals Connector (OSTI)

Noise is a global problem. In 1972 the World Health Organization (WHO) classified noise as a pollutant. Since then most industrialized countries have enacted laws and local regulations to prevent and reduce acoustic environmental pollution. A further aim is to alert people to the dangers of this type of pollution. In this context urban planners need to have tools that allow them to evaluate the degree of acoustic pollution. Scientists in many countries have modeled urban noise using a wide range of approaches but their results have not been as good as expected. This paper describes a model developed for the prediction of environmental urban noise using Soft Computing techniques namely Artificial Neural Networks (ANN). The model is based on the analysis of variables regarded as influential by experts in the field and was applied to data collected on different types of streets. The results were compared to those obtained with other models. The study found that the ANN system was able to predict urban noise with greater accuracy and thus was an improvement over those models. The principal component analysis (PCA) was also used to try to simplify the model. Although there was a slight decline in the accuracy of the results the values obtained were also quite acceptable.

N. Genaro; A. Torija; A. Ramos-Ridao; I. Requena; D. P. Ruiz; M. Zamorano

2010-01-01T23:59:59.000Z

351

Hybrid Artificial Neural Network?Genetic Algorithm Technique for Modeling and Optimization of Plasma Reactor  

Science Journals Connector (OSTI)

(11)?Fissore, D.; Barresi, A. A.; Manca, D. Modelling of Methanol Synthesis in a Network of Forced Unsteady-state Ring Reactor by Artificial Neural Networks for Control Purposes. ... Trials were performed using one or two hidden layers with the no. of neurons varied from 4 to 30. ...

Istadi; N. A. S. Amin

2006-09-02T23:59:59.000Z

352

Torque control of switched reluctance motors based on flexible neural network  

Science Journals Connector (OSTI)

Application of conventional neural network (NN) in modeling and control of switched reluctance motor (SRM) has been limited due to its structure of low degree of freedom, which results in a huge network with large numbers of neurons. In this paper, a ...

Baoming Ge; Aníbal T. de Almeida; Fernando J. T. E. Ferreira

2005-05-01T23:59:59.000Z

353

THE CHOICE OF OPTIMAL STRUCTURE OF ARTIFICIAL NEURAL NETWORK CLASSIFIER INTENDED FOR CLASSIFICATION OF WELDING FLAWS  

SciTech Connect (OSTI)

Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Two weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.

Sikora, R.; Chady, T.; Baniukiewicz, P.; Caryk, M.; Piekarczyk, B. [West Pomeranian University of Technology in Szczecin, Department of Electrical Engineering, 70-313 Szczecin (Poland)

2010-02-22T23:59:59.000Z

354

Prediction of water evaporation rate for indoor swimming hall using neural networks  

Science Journals Connector (OSTI)

Abstract The forecast of water evaporation rate is important in building and energy sectors. However, due to its stochastic nature and complexity, its forecast is rare in the literature. This paper presents a novel neural network approach to predicting water evaporation rate without occupant information for an indoor swimming hall containing five pools in Finland. Input sensitivity is analyzed and two step ahead predictions are compared. The neural networks using water evaporation rate and a binary representation form of time as inputs outperform other models. Experimental data show rapid fluctuations in water evaporation rate during operating hours although relatively stable during non-operating hours. The developed neural network model, however, is able to adapt to fluctuations and reaches good and acceptable accuracies for one- and two-step ahead predictions even for operating hours. The binary form of time simplifies learning process of neural networks. This paper demonstrates the capability of water evaporation rate forecasting without occupant information by neural networks, which might not be possible with traditional empirical models, and their positive impacts on promoting energy efficiency in various applications in general. Finally, the developed method is sufficiently general and can be extended to other systems for forecasting water evaporation rate as well.

Tao Lu; Xiaoshu Lü; Martti Viljanen

2014-01-01T23:59:59.000Z

355

Employment of the artificial neural networks for prediction of magnetic properties of the metallic amorphous alloys  

Science Journals Connector (OSTI)

The aim of this work is to employ the artificial neural networks for modelling the magnetic properties of the amorphous alloys with the iron and cobalt matrix. The artificial neural networks implemented in StatSoft Statistica Neural Network PL 4.0F were used to determine the relationship between the chemical compositions of amorphous alloys, heat treatment parameters and magnetic properties. The attempt to use the artificial neural networks for predicting the effect of the chemical composition and heat treatment parameters on the magnetic flux density BS succeeded, as the level of the obtained results was acceptable, as the level of the obtained results was acceptable. For different magnetic properties of soft magnetic materials, further calculations are planned. The results of calculation makes it possible to design new advantageous combinations of concentrations of the particular elements to develop grades of the soft magnetic alloys. This paper employs the artificial neural networks for modelling chemical composition and heat treatment parameters of amorphous soft magnetic alloys to obtain the best magnetic properties. [Received 15 August 2007; Accepted 20 October 2007

Jaroslaw Konieczny; Leszek A. Dobrzanski; Blazej Tomiczek

2007-01-01T23:59:59.000Z

356

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm  

Science Journals Connector (OSTI)

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these \\{IMFs\\} could be accurately predicted. Finally, the prediction results of all \\{IMFs\\} are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm.

Lean Yu; Shouyang Wang; Kin Keung Lai

2008-01-01T23:59:59.000Z

357

Cell-cycle-dependent variations in FTIR micro-spectra of single proliferating HeLa cells: Principal component and artificial neural network analysis  

E-Print Network [OSTI]

component and artificial neural network analysis Susie Boydston-White a,b , Melissa Romeo a,b , Tatyana.V. All rights reserved. Keywords: Cell cycle; Infrared micro-spectroscopy; Artificial neural network

Sridhar, Srinivas

358

Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems  

SciTech Connect (OSTI)

This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the correlation is a very simple algorithm that can be easily codified in software. Due to its simplicity, it facilitates the necessary process of validation and verification. (authors)

Souza, Rose Mary G.P.; Moreira, Joao M.L. [Centro Tecnologico da Marinha em Sao Paulo - CTMSP, avenida Prof. Lineu Prestes, 2468 - Butanta, Sao Paulo (Brazil)

2006-07-01T23:59:59.000Z

359

Artificial Neural Network Investigation of the Structural Group Contribution Method for Predicting Pure Components Auto Ignition Temperature  

Science Journals Connector (OSTI)

Artificial Neural Network Investigation of the Structural Group Contribution Method for Predicting Pure Components Auto Ignition Temperature ... Artificial neural networks were used to investigate several structural group contribution (SGC) methods available in the literature. ... The hidden layer is a single layer with six neurons, and the output layer consists of one neuron representing the predicted AIT property. ...

Tareq A. Albahri; Reena S. George

2003-09-27T23:59:59.000Z

360

Artificial Neural Network Modeling of Solubilities of 21 Commonly Used Industrial Solid Compounds in Supercritical Carbon Dioxide  

Science Journals Connector (OSTI)

Artificial Neural Network Modeling of Solubilities of 21 Commonly Used Industrial Solid Compounds in Supercritical Carbon Dioxide ... Artificial neural networks are composed of simple elements working in a parallel computational strategy. ... These elements are inspired by biological nervous systems(36-40) and are called neurons. ...

Farhad Gharagheizi; Ali Eslamimanesh; Amir H. Mohammadi; Dominique Richon

2010-11-02T23:59:59.000Z

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361

A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models  

Science Journals Connector (OSTI)

In this paper, we propose a new multi-objective evolutionary algorithm-based ensemble optimizer coupled with neural network models for undertaking feature selection and classification problems. Specifically, the Modified micro Genetic Algorithm (MmGA) ... Keywords: Ensemble models, Evolutionary algorithm, Feature selection, Multi-objective optimization, Neural network classifiers

Choo Jun Tan; Chee Peng Lim; Yu-N Cheah

2014-02-01T23:59:59.000Z

362

Derivation of empirical equations for thermodynamic properties of a ozone safe refrigerant (R404a) using artificial neural network  

Science Journals Connector (OSTI)

This study, deals with the potential application of the artificial neural networks (ANNs) to represent PVTx (pressure-specific volume-temperature-vapor quality) data in the range of temperature of 173-498K and pressure of 10-3600kPa. Generally, numerical ... Keywords: Neural networks, R404a, Thermodynamic properties

Adnan Sözen; Erol Arcaklio?lu; Tayfun Menlik

2010-03-01T23:59:59.000Z

363

Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach  

Science Journals Connector (OSTI)

In this paper, a fault detection and isolation (FDI) scheme for an aircraft jet engine is developed. The proposed FDI system is based on the multiple model approach and utilizes dynamic neural networks (DNNs) to accomplish this goal. Towards this end, ... Keywords: Bank of detection and isolation filters, Dual spool gas turbine engine, Dynamic neural networks, Fault diagnosis, Multiple model scheme

Z. N. Sadough Vanini; K. Khorasani; N. Meskin

2014-02-01T23:59:59.000Z

364

Predictive Modeling of Large-Scale Commercial Water Desalination Plants: Data-Based Neural Network and Model-Based Process  

E-Print Network [OSTI]

Predictive Modeling of Large-Scale Commercial Water Desalination Plants: Data-Based Neural Network for developing predictive models for large-scale commercial water desalination plants by (1) a data (MSF) and reverse osmosis (RO) desalination plants in the world. Our resulting neural network

Liu, Y. A.

365

Big Bend Power Station Neural Network-Intelligent Sootblower (NN-ISB) Optimization  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Big Bend Power Station neural network- Big Bend Power Station neural network- intelligent SootBlower (nn-iSB) oPtimization (comPleted) Project Description The overall goal of this project was to develop a Neural Network-Intelligent Sootblowing (NN-ISB) system on the 445 MW Tampa Electric Big Bend Unit #2 to initiate sootblowing in response to real-time events or conditions within the boiler rather than relying on general rule-based protocols. Other goals were to increase unit efficiency, reduce NO X , and improve stack opacity. In a coal-fired boiler, the buildup of ash and soot on the boiler tubes can lead to a reduction in boiler efficiency. Thus, one of the most important boiler auxiliary operations is the cleaning of heat-absorbing surfaces. Ash and soot deposits are removed by a process known as sootblowing, which uses mechanical devices for on-line cleaning

366

Use of neural networks in the operation of nuclear power plants  

SciTech Connect (OSTI)

Application of neural networks to the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: (a) diagnosing specific abnormal conditions, (b) detection of the change of mode of operation, (c) signal validation, (d) monitoring of check valves, (e) modeling of the plant thermodynamics, (f) emulation of core reload calculations, (g) analysis of temporal sequences in NRC's licensee event report,'' (h) monitoring of plant parameters, and (i) analysis of plant vibrations. Each of these projects and its status are described briefly in this article. the objective of each of these projects is to enhance the safety and performance of nuclear plants through the use of neural networks. 6 refs.

Uhrig, R.E. (Tennessee Univ., Knoxville, TN (USA) Oak Ridge National Lab., TN (USA))

1990-01-01T23:59:59.000Z

367

Applications of neural networks to monitoring and decision making in the operation of nuclear power plants  

SciTech Connect (OSTI)

Application of neural networks to monitoring and decision making in the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: (1) diagnosing specific abnormal conditions or problems in nuclear power plants, (2) detection of the change of mode of operation of the plant, (3) validating signals coming from detectors, (4) review of noise'' data from TVA's Sequoyah Nuclear Power Plant, and (5) examination of the NRC's database of Letter Event Reports'' for correlation of sequences of events in the reported incidents. Each of these projects and its status are described briefly in this paper. This broad based program has as its objective the definition of the state-of-the-art in using neural networks to enhance the performance of commercial nuclear power plants.

Uhrig, R.E. (Tennessee Univ., Knoxville, TN (United States) Oak Ridge National Lab., TN (United States))

1990-01-01T23:59:59.000Z

368

A neural network clustering algorithm for the ATLAS silicon pixel detector  

E-Print Network [OSTI]

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton--proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.

ATLAS collaboration

2014-06-30T23:59:59.000Z

369

Evaluation of artificial neural networks as a model for forecasting consumption of wood products  

Science Journals Connector (OSTI)

In specific sciences, such as forest policy, the need for anticipation becomes more urgent because it has to manage valuable natural resources whose protection and sustainable management is rendered essential. In this paper, a modern method has been used, known as artificial neural networks (ANNs). In order to forecast the necessary future volumes of timber in Greece, a neural network has been developed and trained, using a variety of time series derived from the database of the Food and Agriculture Organisation of the United Nations (FAO) (concerning Greece) as external values and as internal value the Consumer Price Index has been used. Comparing the results of this project with linear and non-linear econometric forecasting models, it has been found that neural networks correspond, as confirmed by the econometric indicators MAPE (average absolute percentage error) and RMSE (the square root of the percentage by the average sum of squares differences).

Giorgos Tigas; Panagiotis Lefakis; Konstantinos Ioannou; Athanasios Hasekioglou

2013-01-01T23:59:59.000Z

370

Over-parameterisation,a major obstacle to the use of artificial neural networks in hydrology ? Hydrology and Earth System Sciences, 7(5), 693706 (2003) EGU  

E-Print Network [OSTI]

Over-parameterisation,a major obstacle to the use of artificial neural networks in hydrology ? 693 to the use of artificial neural networks in hydrology ? Eric Gaume and Raphael Gosset Ecole Nationale des for corresponding author: gaume@cereve.enpc.fr Abstract Recently Feed-Forward Artificial Neural Networks (FNN) have

Paris-Sud XI, Université de

371

ISET Journal of Earthquake Technology, Paper No. 501, Vol. 46, No. 1, March 2009, pp. 1928 ARTIFICIAL NEURAL NETWORK-BASED ESTIMATION OF PEAK  

E-Print Network [OSTI]

­28 ARTIFICIAL NEURAL NETWORK-BASED ESTIMATION OF PEAK GROUND ACCELERATION C.R. Arjun and Ashok Kumar Department the application of artificial neural networks (ANNs) for the estimation of peak ground acceleration (PGA inputs. KEYWORDS: Artificial Neural Networks, Peak Ground Acceleration, Hypocentral Distance, Shear Wave

Gupta, Vinay Kumar

372

Application of an Artificial Neural Network to the Prediction of Firedamp Emissions in Coal Jean-Christophe Couillet ', Marc Prince 2  

E-Print Network [OSTI]

99-72 Application of an Artificial Neural Network to the Prediction of Firedamp Emissions in Coal-linear physical laws and a high number of hardly accessible parameters. So artificial neural networks have been developed to model firedamp emissions : artificial neural networks äs universal approx'imators are able

Paris-Sud XI, Université de

373

Tight bounds on the size of neural networks for classification problems  

SciTech Connect (OSTI)

This paper relies on the entropy of a data-set (i.e., number-of-bits) to prove tight bounds on the size of neural networks solving a classification problem. First, based on a sequence of geometrical steps, the authors constructively compute an upper bound of O(mn) on the number-of-bits for a given data-set - here m is the number of examples and n is the number of dimensions (i.e., R{sup n}). This result is used further in a nonconstructive way to bound the size of neural networks which correctly classify that data-set.

Beiu, V. [Los Alamos National Lab., NM (United States); Pauw, T. de [Universite Catholique de Louvain, Louvain-la-Neuve (Belgium). Dept. de Mathematique

1997-06-01T23:59:59.000Z

374

Development of Ensemble Neural Network Convection Parameterizations for Climate Models  

SciTech Connect (OSTI)

The novel neural network (NN) approach has been formulated and used for development of a NN ensemble stochastic convection parametrization for climate models. This fast parametrization is built based on data from Cloud Resolving Model (CRM) simulations initialized with and forced by TOGA-COARE data. The SAM (System for Atmospheric Modeling), developed by D. Randall, M. Khairoutdinov, and their collaborators, has been used for CRM simulations. The observational data are also used for validation of model simulations. The SAM-simulated data have been averaged and projected onto the GCM space of atmospheric states to implicitly define a stochastic convection parametrization. This parametrization is emulated using an ensemble of NNs. An ensemble of NNs with different NN parameters has been trained and tested. The inherent uncertainty of the stochastic convection parametrization derived in such a way is estimated. Due to these inherent uncertainties, NN ensemble is used to constitute a stochastic NN convection parametrization. The developed NN convection parametrization have been validated in a diagnostic CAM (CAM-NN) run vs. the control CAM run. Actually, CAM inputs have been used, at every time step of the control/original CAM integration, for parallel calculations of the NN convection parametrization (CAM-NN) to produce its outputs as a diagnostic byproduct. Total precipitation (P) and cloudiness (CLD) time series, diurnal cycles, and P and CLD distributions for the large Tropical Pacific Ocean for the parallel CAM-NN and CAM runs show similarity and consistency with the NCEP reanalysis. The P and CLD distributions for the tropical area for the parallel runs have been analyzed first for the TOGA-COARE boreal winter season (November 1992 through February 1993) and then for the winter seasons of the follow-up parallel decadal simulations. The obtained results are encouraging and practically meaningful. They show the validity of the NN approach. This constitutes an important practical conclusion of the study: the obtained results on NN ensembles as a stochastic physics parametrization show a realistic possibility of development of NN convection parametrization for climate (and NWP) models based on learning cloud physics from CRM/SAM simulated data.

Fox-Rabinovitz, M. S.; Krasnopolsky, V. M.

2012-05-02T23:59:59.000Z

375

Feedforward artificial neural network to improve model predictive control in biological processes  

Science Journals Connector (OSTI)

Artificial neural networks (ANNs) offer the versatility of being able to model the dynamics of a biosystem without requiring a phenomenological model. In addition, model predictive control (MPC) is a member of advanced discrete-time process control algorithms. The recent developments in the biotechnology due to MPC utilising the capability of ANN make the practical application of non-linear process control strategies a reality. This paper reviews the recent enhancement and applications of MPC in various biochemical processes using feedforward artificial neural networks which is also known as neural predictive control. The capability of neural predictive control to handle the common problems associated with biochemical processes, namely optimisation of objective function, optimisation of dynamic behaviour of the system, control of ill-defined non-linear systems, improving the computational efficiency of the strategy, disturbance rejection ability and evaluating the control effectiveness are discussed. The review clearly indicates that enormous work has been carried out involving dynamic behaviour of the bioreactor system which is analysed and optimised revealing that feedforward neural network has evolved as a good bioreactor neuro-controller.

Senthil Kumar Arumugasamy; Zainal Ahmad

2011-01-01T23:59:59.000Z

376

Hybrid thermal model for swimming pools based on artificial neural networks for southeast region of Brazil  

Science Journals Connector (OSTI)

Nowadays, the usage of systems based on solar energy have been largely stimulated. The correct designing and efficiency of these systems are highly dependent of the seasonal climatic characteristics of the regions where they will be installed. In this ... Keywords: Artificial neural networks, Hybrid models, Renewable energy, Solar energy systems, Thermal model of pool

Enock T. Santos; Luis E. ZáRate; Elizabeth M. D. Pereira

2013-06-01T23:59:59.000Z

377

DESIGN OF FUEL-ADDITIVES USING HYBRID NEURAL NETWORKS AND EVOLUTIONARY  

E-Print Network [OSTI]

1 DESIGN OF FUEL-ADDITIVES USING HYBRID NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS Anantha Wickliffe, Ohio Abstract Fuel-additives play an important role in deposit reduction on the valves and combustion chamber of the automobile. They reduce cold-start problems, emissions and improve fuel

Venkatasubramanian, Venkat

378

The analysis of circuit breakers kinematics characteristics using the artificial neural networks  

Science Journals Connector (OSTI)

The paper presents the required parameters in the evaluation of the technical state for the High Voltage (HV) circuit breakers. It details some aspects regarding the influence of the kinematics characteristics to the circuit breakers performances. Also, ... Keywords: artificial neural network, circuit breaker, diagnostic, kinematics characteristics, modelling and simulation, monitoring

Maricel Adam; Adrian Baraboi; Catalin Pancu; Sorin Pispiris

2009-02-01T23:59:59.000Z

379

A Neural Network Model for the Tomographic Analysis of Irradiated Nuclear Fuel Rods  

SciTech Connect (OSTI)

A tomographic method based on a multilayer feed-forward artificial neural network is proposed for the reconstruction of gamma-radioactive fission product distribution in irradiated nuclear fuel rods. The quality of the method is investigated as compared to a conventional technique on experimental results concerning a Canada deuterium uranium reactor (CANDU)-type fuel rod irradiated in a TRIGA reactor.

Craciunescu, Teddy [National Institute of Nuclear Physics and Engineering (Romania)

2004-04-15T23:59:59.000Z

380

Spectral basis neural networks for real-time travel time forecasting  

SciTech Connect (OSTI)

This paper examines how real-time information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods ahead (of 5-min duration). The study employed a spectral basis artificial neural network (SNN) that utilizes a sinusoidal transformation technique to increase the linear separability of the input features. Link travel times from Houston that had been collected as part of the automatic vehicle identification system of the TranStar system were used as a test bed. It was found that the SNN outperformed a conventional artificial neural network and gave similar results to that of modular neural networks. However, the SNN requires significantly less effort on the part of the modeler than modular neural networks. The results, of the best SNN were compared with conventional link travel time prediction techniques including a Kalman filtering model, exponential smoothing model, historical profile, and real-time profile. It was found that the SNN gave the best overall results.

Park, D.; Rilett, L.R.; Han, G.

1999-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Artificial Neural Network Approach to Predict the Solubility of C60 in Various Solvents  

Science Journals Connector (OSTI)

Artificial Neural Network Approach to Predict the Solubility of C60 in Various Solvents ... Kohonen ANNs have been widely applied to map a multidimensional parameter space to a two-dimensional array of neurons. ... Therefore, provided that proper molecular parameters were chosen, if two solvents are mapped onto the same (or nearby) neuron the solubility should be close in them. ...

István Z. Kiss; Géza Mándi; Mihály T. Beck

2000-08-03T23:59:59.000Z

382

Non-parametric regression and neural-network inll drilling recovery models for carbonate reservoirs  

E-Print Network [OSTI]

in®ll drilling recovery model is capable of forecasting the oil recovery with less error variance®ll drilling recovery eciency. The approach we take here is stat- istical. It is based on an oil recoveryNon-parametric regression and neural-network in®ll drilling recovery models for carbonate

Valkó, Peter

383

Combining Discriminant Analysis and Neural Networks for Fraud Detection on the Base of  

E-Print Network [OSTI]

Combining Discriminant Analysis and Neural Networks for Fraud Detection on the Base of Complex suspicious, unknown event patterns in the field of fraud detection by using a combi- nation of discriminant of finding unknown fraud patterns, several statistical meth- ods are discussed. On this background, first

Tucci, Sara

384

Durability Assessment of an Arch Dam using Inverse Analysis with Neural Networks and High Performance Computing.  

E-Print Network [OSTI]

the viscoelastic parameters; 3D FEM analysis using High Performance Computing (parallel and vector features) to run Performance Computing. E. M. R. Fairbairn, E. Goulart, A. L. G. A. Coutinho, N. F. F. Ebecken COPPEDurability Assessment of an Arch Dam using Inverse Analysis with Neural Networks and High

Coutinho, Alvaro L. G. A.

385

Statistics of spike trains in conductance-based neural networks: Rigorous results  

E-Print Network [OSTI]

We consider a conductance based neural network inspired by the generalized Integrate and Fire model introduced by Rudolph and Destexhe. We show the existence and uniqueness of a unique Gibbs distribution characterizing spike train statistics. The corresponding Gibbs potential is explicitly computed. These results hold in presence of a time-dependent stimulus and apply therefore to non-stationary dynamics.

B. Cessac

2011-06-23T23:59:59.000Z

386

Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction  

Science Journals Connector (OSTI)

Ensembles have been shown to provide better generalization performance than single models. However, the creation, selection and combination of individual predictors is critical to the success of an ensemble, as each individual model needs to be both ... Keywords: Ensembles, Hybrid multi-objective evolutionary algorithms, Recurrent neural networks, Selection, Time series prediction

Christopher Smith, Yaochu Jin

2014-11-01T23:59:59.000Z

387

CREEP STRENGTH OF HIGH CR FERRITIC STEELS DESIGNED USING NEURAL NETWORKS AND PHASE STABILITY CALCULATIONS  

E-Print Network [OSTI]

CREEP STRENGTH OF HIGH CR FERRITIC STEELS DESIGNED USING NEURAL NETWORKS AND PHASE STABILITY Development of heat-resistant steel for power boilers and turbines has been ongoing for about five decades. This has led to an increase in the thermal efficiency of power plants whenever innovative steels have been

Cambridge, University of

388

SDTC Neural Network Traction Control of an Electric Vehicle without Differential Gears  

E-Print Network [OSTI]

, using two electric in-wheel motors give the possibility to have a torque and speed control in each wheel on the 2Ã?4 electrical vehicles, with independent driving in-wheel motor at the front and with classicalSDTC Neural Network Traction Control of an Electric Vehicle without Differential Gears A. Haddoun1

Paris-Sud XI, Université de

389

The implementation of an intelligent and video-based fall detection system using a neural network  

Science Journals Connector (OSTI)

This paper presents the development of a smart fall detector to minimise accidental falls which occur among elderly people, especially for indoor situations. A video-based detection system was utilised, as this can preserve privacy and monitor the physical ... Keywords: Elderly people, Fall detection, Neural network, Video-based camera

Laila Alhimale, Hussein Zedan, Ali Al-Bayatti

2014-05-01T23:59:59.000Z

390

Meter as Mechanism: A Neural Network that Learns Metrical Michael Gasser, Douglas Eck and Robert Port  

E-Print Network [OSTI]

Meter as Mechanism: A Neural Network that Learns Metrical Patterns Michael Gasser, Douglas Eck that apparently underlies both music and some examples of speech production is meter. Yet detailed measurements of pulses that conform to particular meters. In addition, beginning with an initial state with no biases

Gasser, Michael

391

Evolutionary neural network modeling for forecasting the field failure data of repairable systems  

Science Journals Connector (OSTI)

An accurate product reliability prediction model can not only learn and track the product's reliability and operational performance, but also offer useful information for managers to take follow-up actions to improve the product' quality and cost. This ... Keywords: Genetic algorithms, Neural network model, Reliability prediction, Repairable system

L. Yi-Hui

2007-11-01T23:59:59.000Z

392

header for SPIE use Analysis and Evaluation of Electro-Optic Artificial Neural Network  

E-Print Network [OSTI]

header for SPIE use Analysis and Evaluation of Electro-Optic Artificial Neural Network Performance are represented by the matrix. Optics, with its interferenceless free-space communication capabilities. In electro-optic ANNs, errors due to non-linear or limited accuracy components could exist in the input

Michel, Howard E.

393

A Neural Network Generating Force Command for Motor Control of a Robotic Arm  

E-Print Network [OSTI]

A Neural Network Generating Force Command for Motor Control of a Robotic Arm Antoine de Rengerv is generated and controlled still represents unanswered questions [7]. In [3], an arm controller that learns attractors in the motor space that are used to generate a speed command to control the arm

Paris-Sud XI, Université de

394

Submitted to Handbook of Brain Theory and Neural Networks, 2. Helmholtz Machines  

E-Print Network [OSTI]

Submitted to Handbook of Brain Theory and Neural Networks, 2. Helmholtz Machines and Wake). The Helmholtz machine (Dayan et al, 1995) is an example of an approach to unsupervised learning called analysis the paired images and generators to train a graphics model. In the Helmholtz machine, we attempt to have

Dayan, Peter

395

Determination of thermodynamic properties of an alternative refrigerant (R407c) using artificial neural network  

Science Journals Connector (OSTI)

Thermodynamic analysis of the refrigeration systems is too complex because of thermodynamic properties equations of working fluids, involving the solution of complex differential equations. To simplify this complex process, this paper proposes a new ... Keywords: Artificial neural networks, Ozone safe refrigerant, R407c, Thermodynamic properties

Adnan Sözen; Erol Arcaklio?lu; Tayfun Menlik; Mehmet Özalp

2009-04-01T23:59:59.000Z

396

Knowledge acquisition method from domain text based on theme logic model and artificial neural network  

Science Journals Connector (OSTI)

In order to acquire knowledge from domain text such as failure analysis text of aviation product, a framework is proposed to enhance the efficiency and accuracy of knowledge acquisition. In this framework, sentence templates are defined to extract the ... Keywords: Artificial neural network, Domain text, Failure analysis report, Knowledge acquisition, Theme logic model

Jun Wang; Yunpeng Wu; Xuening Liu; Xiaoying Gao

2010-01-01T23:59:59.000Z

397

Stochastic simulation and spatial estimation with multiple data types using artificial neural networks  

E-Print Network [OSTI]

and spatial estimation with multiple data types using artificial neural networks, Water Resour. Res., 43, W for subsurface mineral, energy, and environmental remediation projects. These methods are used to combine-dimensional geophysical investigations producing enormous data sets that must be managed and processed. As a result

Vermont, University of

398

Smart sensor/actuator node reprogramming in changing environments using a neural network model  

Science Journals Connector (OSTI)

The techniques currently developed for updating software in sensor nodes located in changing environments require usually the use of reprogramming procedures, which clearly increments the costs in terms of time and energy consumption. This work presents ... Keywords: Arduino, Constructive Neural Networks, Microcontroller

Francisco Ortega-Zamorano, José M. Jerez, José L. Subirats, Ignacio Molina, Leonardo Franco

2014-04-01T23:59:59.000Z

399

Simultaneous Learning of Nonlinear Manifolds Based on the Bottleneck Neural Network  

Science Journals Connector (OSTI)

Manifold learning methods are important techniques for nonlinear extraction of high-dimensional data structures. These methods usually extract a global manifold for data. However, in many real-world problems, there is not only one global manifold, but ... Keywords: Deep structure, Manifold learning, Manifold separation, Multitask learning, Neural network, Virtual pattern

Seyyede Zohreh Seyyedsalehi, Seyyed Ali Seyyedsalehi

2014-10-01T23:59:59.000Z

400

Evolutionary optimization of sparsely connected and time-lagged neural networks for time series forecasting  

Science Journals Connector (OSTI)

Time series forecasting (TSF) is an important tool to support decision making (e.g., planning production resources). Artificial neural networks (ANNs) are innate candidates for TSF due to advantages such as nonlinear learning and noise tolerance. However, ... Keywords: Estimation distribution algorithm, Multilayer perceptron, Regression, Time series

Juan Peralta Donate, Paulo Cortez

2014-10-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

A Spiking Neural Network for Gas Discrimination using a Tin Oxide Sensor Array  

E-Print Network [OSTI]

the molecular acivity into electrical signals. This information converges to a recurrent neural network that the different families of ORNs have broadly overlapping tuning profiles related to the molecular quality [6 Recherche en Informatique et Automatique) and by the French consulate, Procore Grant Ref: F-HK19/05T

Paris-Sud XI, Université de

402

874 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 4, JULY 2004 Advanced Search Algorithms for  

E-Print Network [OSTI]

874 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 4, JULY 2004 Advanced Search Algorithms familiar advanced-parameter search algorithms and propose modifications to allow training of systems. This paper addresses required modifications to some of the familiar advanced parameter search algorithms

Slatton, Clint

403

Performance-Oriented Drilling Fluids Design System with a Neural Network Approach  

Science Journals Connector (OSTI)

Drilling fluids play a key role in the minimization of well bore problems when drilling oil or gas wells, usually the design of drilling fluids is depended on many experiments with experience. Rule-based and case-based reasoning drilling fluid system ... Keywords: artificial neural network, drilling fluid, performance-oriented

Yongbin Zhang; Yeli Li; Peng Cao

2009-11-01T23:59:59.000Z

404

ARTIFICIAL NEURAL NETWORK-BASED SEGMENTATION AND APPLE GRADING BY MACHINE VISION  

E-Print Network [OSTI]

ARTIFICIAL NEURAL NETWORK-BASED SEGMENTATION AND APPLE GRADING BY MACHINE VISION Devrim Unay In this paper, a computer vision based system is introduced to automatically sort apple fruits. An artificial. INTRODUCTION Computer vision based quality sorting of apple fruits is nec- essary for increasing the speed

Dupont, Stéphane

405

Apple Defect Detection and Quality Classification with MLP-Neural Networks  

E-Print Network [OSTI]

Apple Defect Detection and Quality Classification with MLP-Neural Networks Devrim UNAY, Bernard' apples is shown. Color, texture and wavelet features are extracted from the apple images. Principal-classifications. The European Union defines three quality classes ("extra", "I", and "II") for the fresh apples

Dupont, Stéphane

406

An Integrated Symbolic and Neural Network Architecture for Machine Learning in the Domain of Nuclear Engineering  

E-Print Network [OSTI]

of Nuclear Engineering Ephraim Nissan Hava Siegelmann Alex Galperin Mathematics Industrial Engineering Nuclear Engineering Bar-Ilan University Technion Ben-Gurion University Ramat-Gan, Israel Haifa, Israel, respectively, expert systems for engineering, and neural networks, we have defined and designed a new phase

Siegelmann , Hava T

407

Gesture Recognition on a Smart Fur Robot Using a Dropout Neural Network to Analyze  

E-Print Network [OSTI]

The technology Smart Fur is a fur-like material. Smart Fur has built-in sensors that record the 2-dimensional in different ways, the overall resistance of the material changes. A picture of Smart Fur is shown in Figure 1 050 051 052 053 Gesture Recognition on a Smart Fur Robot Using a Dropout Neural Network to Analyze

de Freitas, Nando

408

Misplacement of the Left Foot ECG Electrode Detected by Artificial Neural Networks  

E-Print Network [OSTI]

Misplacement of the Left Foot ECG Electrode Detected by Artificial Neural Networks B HedCn', M to be of value in pattern recognition tasks e.g. classiJcation of electrocardiograms (ECGs). Electrocardiographic lead reversals are often overlooked by ECG readers, and may cause incorrectECG interpretation

Ohlsson, Mattias

409

An Artificial Neural Network Approach to the Solution of Molecular Chemical Equilibrium  

E-Print Network [OSTI]

A novel approach is presented for the solution of instantaneous chemical equilibrium problems. The chemical equilibrium can be considered, due to its intrinsically local character, as a mapping of the three-dimensional parameter space spanned by the temperature, hydrogen density and electron density into many one-dimensional spaces representing the number density of each species. We take advantage of the ability of artificial neural networks to approximate non-linear functions and construct neural networks for the fast and efficient solution of the chemical equilibrium problem in typical stellar atmosphere physical conditions. The neural network approach has the advantage of providing an analytic function, which can be rapidly evaluated. The networks are trained with a learning set (that covers the entire parameter space) until a relative error below 1% is reached. It has been verified that the networks are not overtrained by using an additional verification set. The networks are then applied to a snapshot of realistic three-dimensional convection simulations of the solar atmosphere showing good generalization properties.

A. Asensio Ramos; H. Socas-Navarro

2005-05-16T23:59:59.000Z

410

Calculation of transmission system losses for the Taiwan Power Company by the artificial neural network with time decayed weight  

SciTech Connect (OSTI)

For energy conservation and improvement of power system operation efficiency, how to reduce the transmission system losses becomes an important topic of grave concern. To understand the cause, and to evaluate the amount, of the losses are the prior steps to diminish them. To simplify the evaluation procedure without losing too much accuracy, this paper adopts the artificial neural network, which is a model free network, to analyze the transmission system losses. As the artificial neural network with time decayed weight has the capability of learning, memorizing, and forgetting, it is more suitable for a power system with gradually changing characteristics. By using this artificial neural network, the estimation of transmission system losses will be more precise. In this paper, comparison will be made between the results of artificial neural network analysis and polynomial loss equations analysis.

Chu, W.C.; Chen, B.K.; Mo, P.C. [Tatung Inst. of Tech., Taipei (Taiwan, Province of China)

1995-12-31T23:59:59.000Z

411

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts  

E-Print Network [OSTI]

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventio...

Kim, Kyungmin; Hodge, Kari A; Kim, Young-Min; Lee, Chang-Hwan; Lee, Hyun Kyu; Oh, John J; Oh, Sang Hoon; Son, Edwin J

2014-01-01T23:59:59.000Z

412

An artificial neural network strategy for the forward kinematics of robot control  

Science Journals Connector (OSTI)

This paper is devoted to the development and implementation of the neural network technique to solve the forward kinematics problems of robot control, which are mainly singularities and non-linearities. In this paper, a network has been trained to learn the set of end effecter positions X, Y and Z from a given set of joint angle positions for a 6 D.O.F industrial robot. Training data sets were uniformly distributed over a particular region of the robot's working area so that the network can make good generalisation for the intermediate points. Experimental results have shown a good mapping over the working area for the robot. The proposed control technique does not require any prior knowledge of the kinematics model of the system to be controlled; the basic idea of this concept is to use a neural network to learn the characteristics of the robot system rather than having to specify explicit robot system model, which is a significant advantage of using neural network technology. Any modifications in the physical set-up of the system would involve only training the robot in a new path without the need for any major software modification.

Ali T. Hasan; A.M.S. Hamouda; N. Ismail; H.M.A.A. Al-Assadi

2007-01-01T23:59:59.000Z

413

Performance evaluation of CSI-based unified power quality conditioner using artificial neural network  

Science Journals Connector (OSTI)

In recent years unified power quality conditioner (UPQC) is being used as a universal active power conditioning device to mitigate both current as well as voltage harmonics at a distribution end of power system network. The performance of UPQC mainly depends upon how quickly and accurately compensation signals are derived. The artificial neural network (ANN) trained with conventional compensator data, can deliver compensation signals more accurately and quickly than conventional compensator at varied load condition. This paper presents performance verification of CSI-based UPQC using artificial neural network. The ANN-based compensation system eliminates voltage as well as current harmonics with good dynamic response. Extensive simulation results using Matlab/Simulink for RL load connected through an uncontrolled bridge rectifier validates the performance of ANN compensator.

K. Vadirajacharya; P. Agarwal; H.O. Gupta

2008-01-01T23:59:59.000Z

414

3-D Inversion Of Borehole-To-Surface Electrical Data Using A...  

Open Energy Info (EERE)

Electrical Data Using A Back-Propagation Neural Network Abstract The "fluid-flow tomography", an advanced technique for geoelectrical survey based on the conventional...

415

Wind Conditions in a Fjordlike Bay and Predictions of Wind Speed Using Neighboring Stations Employing Neural Network Models  

Science Journals Connector (OSTI)

This paper evaluates the applicability of neural networks for estimating wind speeds at various target locations using neighboring reference locations along the south coast of Newfoundland, Canada. The stations were chosen to cover a variety of ...

Jens J. Currie; Pierre J. Goulet; Andry W. Ratsimandresy

2014-06-01T23:59:59.000Z

416

Research on Short-term Load Forecasting of the Thermoelectric Boiler Based on a Dynamic RBF Neural Network  

E-Print Network [OSTI]

As thermal inertia is the key factor for the lag of thermoelectric utility regulation, it becomes very important to forecast its short-term load according to running parameters. In this paper, dynamic radial basis function (RBF) neural network...

Dai, W.; Zou, P.; Yan, C.

2006-01-01T23:59:59.000Z

417

Using a Self Organizing Map Neural Network for Short-Term Load Forecasting, Analysis of Different Input Data Patterns  

Science Journals Connector (OSTI)

This research uses a Self-Organizing Map neural network model (SOM) as a short-term forecasting method. The objective is to obtain the demand curve of certain hours of the next day. In order to validate the model...

C. Senabre; S. Valero; J. Aparicio

2010-01-01T23:59:59.000Z

418

The Study on Oil Prices’ Effect on International Gas Prices Based on Using Wavelet Based Boltzmann Cooperative Neural Network  

Science Journals Connector (OSTI)

In this paper, we build up WBNNK model based on wavelet-based cooperative Boltzmann neural network and kernel density estimation. The international oil prices time series is decomposed into approximate components...

Xiazi Yi; Zhen Wang

2013-01-01T23:59:59.000Z

419

A neural network based approach to estimate of power system harmonics for an induction furnace under the different load conditions  

Science Journals Connector (OSTI)

This study presents an artificial neural network based intelligent monitoring algorithm to detect of a power system harmonics. The proposed approach was tested on the current and voltage data of an induction furn...

Hayrettin Gokozan; Sezai Taskin; Serhat Seker; Huseyin Ekiz

2014-11-01T23:59:59.000Z

420

Nonlinear Neural Network-Based Mixture Model for Estimating the Concentration of Nitrogen Salts in Turbid Inland Waters Using  

E-Print Network [OSTI]

by agricultural and industrial sources. The proposed neural network architecture consists of a modified multi of oceans, rivers, lakes, snow and glaciers. As a result, water pollution represents a major enviromental

Plaza, Antonio J.

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

Investigation of the operation process of a repairable technical object in an expert servicing system with an artificial neural network  

Science Journals Connector (OSTI)

This paper presents a method to investigate the effectiveness of an operation process with a servicing expert system including an artificial neural network. A method ... basis was presented of the modelling of an...

Stanis?aw Duer

2010-07-01T23:59:59.000Z

422

Neural network approaches to tracer identification as related to PIV research  

SciTech Connect (OSTI)

Neural networks have become very powerful tools in many fields of interest. This thesis examines the application of neural networks to another rapidly growing field flow visualization. Flow visualization research is used to experimentally determine how fluids behave and to verify computational results obtained analytically. A form of flow visualization, particle image velocimetry (PIV). determines the flow movement by tracking neutrally buoyant particles suspended in the fluid. PIV research has begun to improve rapidly with the advent of digital imagers, which can quickly digitize an image into arrays of grey levels. These grey level arrays are analyzed to determine the location of the tracer particles. Once the particles positions have been determined across multiple image frames, it is possible to track their movements, and hence, the flow of the fluid. This thesis explores the potential of several different neural networks to identify the positions of the tracer particles. Among these networks are Backpropagation, Kohonen (counter-propagation), and Cellular. Each of these algorithms were employed in their basic form, and training and testing were performed on a synthetic grey level array. Modifications were then made to them in attempts to improve the results.

Seeley, C.H. Jr.

1992-12-01T23:59:59.000Z

423

Optimization of operating conditions for steam turbine using an artificial neural network inverse  

Science Journals Connector (OSTI)

Abstract The useful life (UL) of the failure assessment in blades of steam turbines is optimized using the artificial intelligence. The objective of this paper is to develop an integrated approach using artificial neural network inverse (ANNi) coupling with a Nelder Mead optimization method to estimate the resonance stress when the UL of the blades is required. The proposed method \\{ANNi\\} is a new tool which inverts the artificial neural network (ANN). Firstly, It is necessary to build the artificial neural network (ANN) that simulates the output parameter (UL). ANN's model is constituted of feedforward network with one hidden layer to calculate the output of the process when input parameters are well known, then inverting ANN. The \\{ANNi\\} could be used as a tool to estimate the optimal unknown parameter required (resonance stress). Very low percentage of error and short computing time are precise and efficient, make this methodology (ANNi) attractive to be applied for control on line the UL of the system and constitutes a very promising framework for finding set of “good solutions”.

Y.El. Hamzaoui; J.A. Rodríguez; J.A. Hernández; Victor Salazar

2015-01-01T23:59:59.000Z

424

Lithology determination from well logs with fuzzy associative memory neural network  

SciTech Connect (OSTI)

An artificial intelligence technique of fuzzy associative memory is used to determine rock types from well-log signatures. Fuzzy associative memory (FAM) is a hybrid of neutral network and fuzzy expert system. This new approach combines the learning ability of neural network and the strengths of fuzzy linguistic modeling to adaptively infer lithologies from well-log signatures based on (1) the relationships between the lithology and log signature that the neural network have learned during the training and/or (2) geologist`s knowledge about the rocks. The method is applied to a sequence of the Ordovician rock units in northern Kansas. This paper also compares the performances of two different methods, using the same data set for meaningful comparison. The advantages of FAM are (1) expert knowledge acquired by geologists is fully utilized; (2) this knowledge is augmented by the neural network learning from the data, when available; and (3) FAM is transparent in that the knowledge is explicitly stated in the fuzzy rules.

Chang, H.C.; Chen, H.C.; Fang, J.H. [Univ. of Alabama, Tuscaloosa, AL (United States)

1997-05-01T23:59:59.000Z

425

Neural Networks Bridge Clean Air Act Gaps - PETC Review, Winter 1996  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

NEURAL NETWORKS BRIDGE NEURAL NETWORKS BRIDGE CLEAN AIR ACT GAPS Making strides in previously untapped areas as environmental restrictions tighten, computer software is claiming an increasingly dominant role in power plant operations that can help utilities and industrial sources ensure emissions compliance. And in some cases, artificial intelligence can heighten the effectiveness of clean coal technologies without inflating costs, rewarding coal-burning utilities that use clean coal technology-style equipment while potentially widening the market for clean coal systems overall. PETC, during the past five years, has pursued scale, PETC researchers are testing artificial an aggressive, $2 million-plus, research cam- intelligence systems' ability to estimate ash paign that focuses on developing simple ways

426

Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study  

SciTech Connect (OSTI)

Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curve (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions.

Hooman, A.; Mohammadzadeh, M

2008-01-30T23:59:59.000Z

427

An artificial neural network based $b$ jet identification algorithm at the CDF Experiment  

E-Print Network [OSTI]

We present the development and validation of a new multivariate $b$ jet identification algorithm ("$b$ tagger") used at the CDF experiment at the Fermilab Tevatron. At collider experiments, $b$ taggers allow one to distinguish particle jets containing $B$ hadrons from other jets. Employing feed-forward neural network architectures, this tagger is unique in its emphasis on using information from individual tracks. This tagger not only contains the usual advantages of a multivariate technique such as maximal use of information in a jet and tunable purity/efficiency operating points, but is also capable of evaluating jets with only a single track. To demonstrate the effectiveness of the tagger, we employ a novel method wherein we calculate the false tag rate and tag efficiency as a function of the placement of a lower threshold on a jet's neural network output value in $Z+1$ jet and $t\\bar{t}$ candidate samples, rich in light flavor and $b$ jets, respectively.

J. Freeman; W. Ketchum; J. D. Lewis; S. Poprocki; A. Pronko; V. Rusu; P. Wittich

2011-08-24T23:59:59.000Z

428

The Efficacy of Galaxy Shape Parameters in Photometric Redshift Estimation: A Neural Network Approach  

SciTech Connect (OSTI)

We present a determination of the effects of including galaxy morphological parameters in photometric redshift estimation with an artificial neural network method. Neural networks, which recognize patterns in the information content of data in an unbiased way, can be a useful estimator of the additional information contained in extra parameters, such as those describing morphology, if the input data are treated on an equal footing. We show that certain principal components of the morphology information are correlated with galaxy type. However, we find that for the data used the inclusion of morphological information does not have a statistically significant benefit for photometric redshift estimation with the techniques employed here. The inclusion of these parameters may result in a trade-off between extra information and additional noise, with the additional noise becoming more dominant as more parameters are added.

Singal, J.; Shmakova, M.; Gerke, B.; /KIPAC, Menlo Park /SLAC /Stanford U.; Griffith, R.L.; /Caltech, JPL; Lotz, J.; /NOAO, Tucson

2011-05-20T23:59:59.000Z

429

AN ARTIFICIAL NEURAL NETWORK EVALUATION OF TUBERCULOSIS USING GENETIC AND PHYSIOLOGICAL PATIENT DATA  

SciTech Connect (OSTI)

When doctors see more cases of patients with tell-tale symptoms of a disease, it is hoped that they will be able to recognize an infection administer treatment appropriately, thereby speeding up recovery for sick patients. We hope that our studies can aid in the detection of tuberculosis by using a computer model called an artificial neural network. Our model looks at patients with and without tuberculosis (TB). The data that the neural network examined came from the following: patient' age, gender, place, of birth, blood type, Rhesus (Rh) factor, and genes of the human Leukocyte Antigens (HLA) system (9q34.1) present in the Major Histocompatibility Complex. With availability in genetic data and good research, we hope to give them an advantage in the detection of tuberculosis. We try to mimic the doctor's experience with a computer test, which will learn from patient data the factors that contribute to TB.

Griffin, William O.; Darsey, Jerry A. [Department of Chemistry of Arkansas at Little Rock, Little Rock, AR (United States); Hanna, Josh [Department of Bioinformatics of Arkansas at Little Rock, Little Rock, AR (United States); Razorilova, Svetlana; Kitaev, Mikhael; Alisherov, Avtandiil [National Center of Tuberculosis, Bishkek (Kyrgyzstan); Tarasenko, Olga [Department of Biology University of Arkansas at Little Rock, Little Rock, AR (United States)

2010-04-12T23:59:59.000Z

430

Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network  

E-Print Network [OSTI]

This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).

Goutami Chattopadhyay; Surajit Chattopadhyay

2012-04-18T23:59:59.000Z

431

Fusion of artificial neural network and fuzzy system for short term weather forecasting  

Science Journals Connector (OSTI)

Weather forecasting is the challenging problem for the modern life. Some researches have been conducted to design the accurate prediction in some past years but still it is incomplete. In this paper, we propose the system of short period weather forecasting designed based on the current weather parameter consisted of temperature, humidity, air pressure, wind direction and speed and present weather condition. This system uses fusion of feed forward artificial neural network (ANN) and fuzzy system architecture as main algorithm of weather prediction, Lavendberg-Marquadt as learning algorithm and fuzzy C-mean (FCM) as clustering method in initialisation step. Based on the system architecture, this method can predict the weather continuously despite the change of unpredictable patterns. Furthermore, this system has clear reasoning logic on the fuzzy logic instead of its adaptation ability on its neural network architecture. The performance of proposed system has accuracy up to 78% for validity among three possible weathers, i.e., shiny, cloudy and rainy.

Budiman Putra; Bagus Tris Atmaja; Syahroni Hidayat

2012-01-01T23:59:59.000Z

432

Gene identification and analysis: an application of neural network-based information fusion  

SciTech Connect (OSTI)

Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a gene localization and modeling system called GRAIL. GRAIL is a multiple sensor-neural network based system. It localizes genes in anonymous DNA sequence by recognizing gene features related to protein-coding slice sites, and then combines the recognized features using a neural network system. Localized coding regions are then optimally parsed into a gene mode. RNA polymerase II promoters can also be predicted. Through years of extensive testing, GRAIL consistently localizes about 90 percent of coding portions of test genes with a false positive rate of about 10 percent. A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA.

Matis, S.; Xu, Y.; Shah, M.B.; Mural, R.J.; Einstein, J.R.; Uberbacher, E.C.

1996-10-01T23:59:59.000Z

433

Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier  

E-Print Network [OSTI]

Abstract—In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (?, ?, ?, ? and ?) and the Parseval’s theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.

I. Omerhodzic; S. Avdakovic; A. Nuhanovic; K. Dizdarevic

434

A practical guide to neural nets  

SciTech Connect (OSTI)

The concept of neural networks, their operation, and applications are reviewed. Topics discussed include definitions, terminology, and concepts of neural networks, the principal issues and problems addressed by neural network technology, recent developments in the field of artificial intelligence, characteristics and limitations of neural networks, and various neural network architectures. Other topics covered include the basic learning mechanisms of neural networks, examples of neural network applications, implementations of neural networks, some current problems in neural network research, and suggestions for future research. 126 refs.

Nelson, M.M.; Illingworth, W.T.

1991-01-01T23:59:59.000Z

435

Multi-Column Deep Neural Network for Traffic Sign Classification  

E-Print Network [OSTI]

are then repeatedly pooled and re- filtered, resulting in a deep feed-forward network architecture whose output was the Neocognitron by Fukushima (1980), which inspired many of the more recent variants. Unsupervised learning

Schmidhuber, Juergen

436

PyNN: A Python API for Neural Network Modeling  

Science Journals Connector (OSTI)

PyNN is an application programming interface (API) for describing and simulating neuronal network models...1997), NEST (Gewaltig and Diesmann 2007), Brian (Goodman and Brette 2008), or PCSIM (Pecevski et al. 2009

Dr. Andrew P. Davison

2014-05-01T23:59:59.000Z

437

Short-term load forecasting using generalized regression and probabilistic neural networks in the electricity market  

SciTech Connect (OSTI)

For the economic and secure operation of power systems, a precise short-term load forecasting technique is essential. Modern load forecasting techniques - especially artificial neural network methods - are particularly attractive, as they have the ability to handle the non-linear relationships between load, weather temperature, and the factors affecting them directly. A test of two different ANN models on data from Australia's Victoria market is promising. (author)

Tripathi, M.M.; Upadhyay, K.G.; Singh, S.N.

2008-11-15T23:59:59.000Z

438

Artificial Neural Networks as Non-Linear Extensions of Statistical Methods in Astronomy  

E-Print Network [OSTI]

We attempt to de-mistify Artificial Neural Networks (ANNs) by considering special cases which are related to other statistical methods common in Astronomy and other fields. In particular we show how ANNs generalise Bayesian methods, multi-parameter fitting, Principal Component Analysis (PCA), Wiener filtering and regularisation methods. Examples of morphological classification of galaxies illustrate how non-linear ANNs improve on linear techniques.

Ofer Lahav

1994-11-17T23:59:59.000Z

439

Digital Pulseshape Analysis by Neural Networks for the Heidelberg-Moscow-Double-Beta-Decay-Experiment  

E-Print Network [OSTI]

The Heidelberg-Moscow Experiment is presently the most sensitive experiment looking for neutrinoless double-beta decay. Recently the already very low background has been lowered by means of a Digital Pulseshape Analysis using a one parameter cut to distinguish between pointlike events and multiple scattered events. To use all the information contained in a recorded digital pulse, we developed a new technique for event recognition based on neural networks.

B. Majorovits; H. V. Klapdor-Kleingrothaus

1999-11-02T23:59:59.000Z

440

This paper presents a neural network based technique for the solution of a water system state estimation problem.The technique  

E-Print Network [OSTI]

ABSTRACT This paper presents a neural network based technique for the solution of a water system applied to a realistic 34-node water network. By changing the values of neural network parameters both with respect to their sensitivity to measurement errors. INTRODUCTION Efficient control of a complex water

Bargiela, Andrzej

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

Training a Spiking Neural Network to Control a 4-DoF Robotic Arm based on Spike Timing-Dependent Plasticity  

E-Print Network [OSTI]

Training a Spiking Neural Network to Control a 4-DoF Robotic Arm based on Spike Timing network architecture that autonomously learns to control a 4 degree-of- freedom robotic arm after of the arm of an iCub humanoid robot. I. INTRODUCTION IN this work, we present a neural network architecture

Shanahan, Murray

442

Closed loop adaptive control of spectrum-producing step using neural networks  

DOE Patents [OSTI]

Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller.

Fu, Chi Yung (San Francisco, CA)

1998-01-01T23:59:59.000Z

443

Artificial neural network modelling for evaluating austenitic stainless steel and Zircaloy-2 welds  

Science Journals Connector (OSTI)

Ferrite content in austenitic stainless steel welds is a measure of resistance to solidification cracking. Accurate estimation of ferrite content in austenitic stainless steel welds is important to ensure crack free welds. An artificial neural network (ANN) model has been developed to predict ferrite number with an improved accuracy. Eddy current (EC) testing is attractive due to high sensitivity and versatility for the detection of harmful surface defects. Artificial neural network modelling has been used to process the eddy current data for evaluating the defect depth so that on-line eddy current testing is possible in austenitic stainless steel welds. There is a necessity to develop on-line monitoring methods for evaluation the quality of spacer pad welds in cladding tubes made of Zircaloy-2 used in pressurized heavy water reactors (PHWR). Shear strength values of the individual coins is the measure of the quality of the welds. Prediction of shear strength values of the individual coins ensures their integrity. Artificial neural network model has been developed for prediction of shear strength of spacer pad welds of Zircaloy-2.

M. Vasudevan; B.P.C. Rao; B. Venkatraman; T. Jayakumar; Baldev Raj

2005-01-01T23:59:59.000Z

444

Classifying tensile strength of HSLA steel: an investigation through neural networks using Mahalanobis Distance  

Science Journals Connector (OSTI)

This paper addresses a comparative approach of classification of Thermomechanically Controlled Processed (TMCP) High Strength Low Alloy (HSLA) steels based on Mahalanobis-Taguchi System (MTS) principles and ensemble neural networks, including sensitivity analysis for variable selection. Later, a hybrid approach is developed, depending on the ability of Mahalanobis Distance (MD) in capturing the correlation structure of a multi-dimensional system, both for the Multi-Layered Perceptron (MLP) and for Radial Basis Function (RBF) networks. The results are found to be quite consistent in describing the role of input parameters for effective classification of such steel.

Prasun Das; Shubhabrata Datta; Bidyut Kr. Bhattacharyay

2010-01-01T23:59:59.000Z

445

Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification  

E-Print Network [OSTI]

The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics.

Byron P. Roe; Hai-Jun Yang; Ji Zhu; Yong Liu; Ion Stancu; Gordon McGregor

2004-08-30T23:59:59.000Z

446

Nonlinear reduction of high-dimensional dynamical systems via neural networks  

Science Journals Connector (OSTI)

A technique for empirically determining optimal coordinates for modeling a dynamical system is presented. The methodology may be viewed as a nonlinear extension of the Karhunen-Loève procedure and is implemented via an autoassociative neural network. Given a high-dimensional system of differential equations which model a dynamical system asymptotically residing on a stable attractor, the task of the network is to compute a reembedding of the attractor and the dynamics into an ambient space which reflects the intrinsic dimensionality of the problem. The method is demonstrated on the unforced Van der Pol oscillator, the forced Van der Pol, and the Kuramoto-Sivashinsky equation.

Michael Kirby and Rick Miranda

1994-03-21T23:59:59.000Z

447

AUTOMATED MORPHOLOGICAL CLASSIFICATION OF APM GALAXIES BY SUPERVISED ARTIFICIAL NEURAL NETWORKS  

E-Print Network [OSTI]

We train Artificial Neural Networks to classify galaxies based solely on the morphology of the galaxy images as they appear on blue survey plates. The images are reduced and morphological features such as bulge size and the number of arms are extracted, all in a fully automated manner. The galaxy sample was first classified by 6 independent experts. We use several definitions for the mean type of each galaxy, based on those classifications. We then train and test the network on these features. We find that the rms error of the network classifications, as compared with the mean types of the expert classifications, is 1.8 Revised Hubble Types. This is comparable to the overall rms dispersion between the experts. This result is robust and almost completely independent of the network architecture used.

A. Naim; O. Lahav; L. Sodre Jr.; M. C. Storrie-Lombardi

1995-03-01T23:59:59.000Z

448

A new symbiotic evolution-based fuzzy-neural approach to fault diagnosis of marine propulsion systems  

Science Journals Connector (OSTI)

This paper presents a symbiotic evolution-based fuzzy-neural diagnostic system (SE-FNDS) for fault diagnosis of propeller–shaft marine propulsion systems. The SE-FNDS combination of fuzzy modeling, back-propagation training and symbiotic evolution function auto-generates its own optimal fuzzy-neural architecture, a significant advantage over previous time-consuming manual parameter determination. Four hundred samples from a test propeller–shaft system are taken over a range of 100–500 rpm, during normal and experimentally induced faulty operation. This database is applied as input/output rule generation and training data for the fuzzy-neural network. Comparison of system construction time and diagnostic accuracy is made by applying the same database to SE-FNDS and four traditional systems. Compared to traditional methods, diagnostic decisions from SE-FNDS show 94.17% agreement with real conditions and less CPU time for system construction. Two nonlinear function approximations are also used to demonstrate the proposed system. The presented design is useful as a core module for more advanced computer-assisted diagnostic systems and for direct application in marine propulsion systems.

Hsing-Chia Kuo; Hui-Kuo Chang

2004-01-01T23:59:59.000Z

449

Generating Coherent Patterns of Activity from Chaotic Neural Networks  

E-Print Network [OSTI]

that can be switched by control inputs, and motor patterns matching human motion capture data. Our results may be a more rapid and powerful modulator of network activity than generally appreciated. Researchers in the machine learning and computer vision communities have developed powerful methods

Columbia University

450

A Neural Network Based Predictive Mechanism for Available Bandwidth  

E-Print Network [OSTI]

tested on classical trace files and compared with the well-known system NWS (Network Weather Service that minimizes duplicate packets and latency while maximizing bandwidth [2]. Two throughput metrics Size (VPS) probing methodology that measures the hop- by-hop metrics, and Packet Train Dispersion (PTD

Sun, Xian-He

451

Applications of Artificial Neural Network for the Prediction of Pool Boiling Curves  

SciTech Connect (OSTI)

Artificial neural network (ANN) has the advantage that the best-fit correlations of experimental data will no longer be necessary for predicting unknowns from the known parameters. The ANN was applied to predict the pool boiling curves in this paper. The database of experimental data presented by Berenson, Dhuga et al., and Bui and Dhir etc. were used in the analysis. The database is subdivided in two subsets. The first subset is used to train the network and the second one is used to test the network after the training process. The input parameters of the ANN are: wall superheat {delta}T{sub w}, surface roughness, steady/transient heating/transient cooling, subcooling, Surface inclination and pressure. The output parameter is heat flux q. The proposed methodology allows us to achieve the accuracy that satisfies the user's convergence criterion and it is suitable for pool boiling curve data processing. (authors)

Su, Guanghui; Fukuda, K.; Morita, K. [Kyushu University, 6-10-1, Hakozaki, Higashi-ku, Fukuoka 812-81 (Japan)

2002-07-01T23:59:59.000Z

452

Application of holographic neural networks for flue gas emissions prediction in the Burnaby incinerator  

SciTech Connect (OSTI)

This article describes the development of a parametric prediction system (PPS) for various emission species at the Burnaby incinerator. The continuous emissions monitoring system at the Burnaby incinerator is shared between three boilers and therefore actual results are only available 5 minutes out of every 15 minutes. The PPS was developed to fill in data for the 10 minutes when the Continuous Emission Monitor (CEM) is measuring the other boilers. It bases its prediction on the last few actual readings taken and parametrically predicts CO, SO2 and NOx. The Burnaby Incinerator is located in the commercial/industrial area of South Burnaby, British Columbia. It consists of three separate lines, each burning ten tonnes of garbage per hour and producing about three tonnes of steam for every tonne of garbage burned. The air pollution control system first cools the combustion products with water injection and then scrubs them with very fine hydrated lime. Carbon is added to the lime to enhance the scrubbing of the combustion products. The CEM monitors the levels of oxygen, carbon monoxide, nitrogen oxides, sulphur dioxide and opacity. In 1996, an expert system was installed on one of boilers at the Burnaby Incinerator plant to determine if it could improve the plant=s operations and reduce overall emission. As part of the expert system, the PPS was developed. Holographic Neural Technology (HNeT), developed by AND Corporation of Toronto, Ontario, is a novel neural network technology using complex numbers in its architecture. Compared to the traditional neural networks, HNeT has some significant advantage. It is more resilient against converging on local minima; is faster training and executing; less prone to over fitting; and, in most cases, has significantly lower error. Selection of independent variabs, training set preparation, testing neural nets and other related issue will be discussed.

Zheng, L.; Dockrill, P.; Clements, B. [Natural Resources Canada, Nepean, Ontario (Canada). CANMET Energy Technology Centre

1997-12-31T23:59:59.000Z

453

Cellular Neural Network for Real Time Image Processing  

SciTech Connect (OSTI)

Since their introduction in 1988, Cellular Nonlinear Networks (CNNs) have found a key role as image processing instruments. Thanks to their structure they are able of processing individual pixels in a parallel way providing fast image processing capabilities that has been applied to a wide range of field among which nuclear fusion. In the last years, indeed, visible and infrared video cameras have become more and more important in tokamak fusion experiments for the twofold aim of understanding the physics and monitoring the safety of the operation. Examining the output of these cameras in real-time can provide significant information for plasma control and safety of the machines. The potentiality of CNNs can be exploited to this aim. To demonstrate the feasibility of the approach, CNN image processing has been applied to several tasks both at the Frascati Tokamak Upgrade (FTU) and the Joint European Torus (JET)

Vagliasindi, G.; Arena, P.; Fortuna, L. [Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi - Universita degli Studi di Catania, I-95125 Catania (Italy); Mazzitelli, G. [ENEA-Gestione Grandi Impianti Sperimentali, via E. Fermi 45, I-00044 Frascati, Rome (Italy); Murari, A. [Consorzio RFX-Associazione EURATOM ENEA per la Fusione, I-35127 Padova (Italy)

2008-03-12T23:59:59.000Z

454

Simulation of flood flow in a river system using artificial neural networks Hydrology and Earth System Sciences, 9(4), 313321 (2005) EGU  

E-Print Network [OSTI]

Simulation of flood flow in a river system using artificial neural networks 313 Hydrology and Earth System Sciences, 9(4), 313321 (2005) © EGU Simulation of flood flow in a river system using artificial Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation

Paris-Sud XI, Université de

455

On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications  

SciTech Connect (OSTI)

In this study we address the problem of predicting the position of a moving lung tumor during respiration on the basis of external breathing signals--a technique used for beam gating, tracking, and other dynamic motion management techniques in radiation therapy. We demonstrate the use of neural network filters to correlate tumor position with external surrogate markers while simultaneously predicting the motion ahead in time, for situations in which neither the breathing pattern nor the correlation between moving anatomical elements is constant in time. One pancreatic cancer patient and two lung cancer patients with mid/upper lobe tumors were fluoroscopically imaged to observe tumor motion synchronously with the movement of external chest markers during free breathing. The external marker position was provided as input to a feed-forward neural network that correlated the marker and tumor movement to predict the tumor position up to 800 ms in advance. The predicted tumor position was compared to its observed position to establish the accuracy with which the filter could dynamically track tumor motion under nonstationary conditions. These results were compared to simplified linear versions of the filter. The two lung cancer patients exhibited complex respiratory behavior in which the correlation between surrogate marker and tumor position changed with each cycle of breathing. By automatically and continuously adjusting its parameters to the observations, the neural network achieved better tracking accuracy than the fixed and adaptive linear filters. Variability and instability in human respiration complicate the task of predicting tumor position from surrogate breathing signals. Our results show that adaptive signal-processing filters can provide more accurate tumor position estimates than simpler stationary filters when presented with nonstationary breathing motion.

Isaksson, Marcus; Jalden, Joakim; Murphy, Martin J. [Department of Electrical Engineering, Stanford University, Stanford, California 94036 (United States); Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298 (United States)

2005-12-15T23:59:59.000Z

456

Applications of neural networks to monitoring and decision making in the operation of nuclear power plants. Summary  

SciTech Connect (OSTI)

Application of neural networks to monitoring and decision making in the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: (1) diagnosing specific abnormal conditions or problems in nuclear power plants, (2) detection of the change of mode of operation of the plant, (3) validating signals coming from detectors, (4) review of ``noise`` data from TVA`s Sequoyah Nuclear Power Plant, and (5) examination of the NRC`s database of ``Letter Event Reports`` for correlation of sequences of events in the reported incidents. Each of these projects and its status are described briefly in this paper. This broad based program has as its objective the definition of the state-of-the-art in using neural networks to enhance the performance of commercial nuclear power plants.

Uhrig, R.E. [Tennessee Univ., Knoxville, TN (United States)]|[Oak Ridge National Lab., TN (United States)

1990-12-31T23:59:59.000Z

457

M. A. Sartori and P. J. Antsaklis, "Neural Network Implementations for Control Scheduling," Technical Report #91-04-02, Dept. of Electrical Engineering, University of Notre Dame, April 1991.  

E-Print Network [OSTI]

M. A. Sartori and P. J. Antsaklis, "Neural Network Implementations for Control Scheduling. Sartori and P. J. Antsaklis, "Neural Network Implementations for Control Scheduling," Technical Report #91. Antsaklis, "Neural Network Implementations for Control Scheduling," Technical Report #91-04-02, Dept

Antsaklis, Panos

458

Chai, S.S., Veenendaal,B., West G. and J.P. Walker (2009). Input Parameter Selection for Soil Moisture Retrieval Using an Artificial Neural Network. In: Ostendorf, B., Baldock, P., Bruce, D., Burdett, M. and P. Corcoran (eds.),  

E-Print Network [OSTI]

Moisture Retrieval Using an Artificial Neural Network. In: Ostendorf, B., Baldock, P., Bruce, D., Burdett-0-9581366-8-6. INPUT PARAMETERS SELECTION FOR SOIL MOISTURE RETRIEVAL USING AN ARTIFICIAL NEURAL NETWORK Soo-See Chai 1-linear and ill-posed problem. Artificial Neural Networks (ANNs) have been demonstrated to be good solutions

Walker, Jeff

459

The Artificial Neural Networks as a tool for analysis of the individual Extensive Air Showers data  

E-Print Network [OSTI]

In that paper we discuss possibilities of using the Artificial Neural Network technic for the individual Extensive Air Showers data evaluation. It is shown that the recently developed new computational methods can be used in studies of EAS registered by very large and complex detector systems. The ANN can be used to classify showers due to e.g. primary particle mass as well as to find a particular EAS parameter like e.g. total muon number. The examples of both kinds of analysis are given and discussed.

Tadeusz Wibig

1996-08-03T23:59:59.000Z

460

Artificial Neural Network Solutions of Slab-Geometry Neutron Diffusion Problems  

SciTech Connect (OSTI)

Artificial neural network (ANN) methods have been researched extensively within the nuclear community for applications in systems control, diagnostics, and signal processing. We consider here the use of multilayer perceptron ANNs as an alternative to finite-difference and finite-element methods for obtaining solutions to neutron diffusion problems. This work is based on a method proposed by van Milligen et. al. to obtain solutions of the differential equations arising in plasma physics applications. This ANN method has the potential advantage of yielding an accurate, differentiable approximation to the solution of diffusion problems at all points in the spatial domain.

Brantley, P.S.

2000-06-12T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

Quantitative analysis of tin alloy combined with artificial neural network prediction  

SciTech Connect (OSTI)

Laser-induced breakdown spectroscopy was applied to quantitative analysis of three impurities in Sn alloy. The impurities analysis was based on the internal standard method using the Sn I 333.062-nm line as the reference line to achieve the best reproducible results. Minor-element concentrations (Ag, Cu, Pb) in the alloy were comparatively evaluated by artificial neural networks (ANNs) and calibration curves. ANN was found to effectively predict elemental concentrations with a trend of nonlinear growth due to self-absorption. The limits of detection for Ag, Cu, and Pb in Sn alloy were determined to be 29, 197, and 213 ppm, respectively.

Oh, Seong Y.; Yueh, Fang-Yu; Singh, Jagdish P.

2010-05-01T23:59:59.000Z

462

Control the springback of metal sheets by using an artificial neural network  

SciTech Connect (OSTI)

One of the greatest challenges of manufacturing sheet metal parts is to obtain consistent parts dimensions. Springback is the major cause of variations and inconsistencies in the final part geometry. Obtaining a consistent and desirable amount of springback is extremely difficult due to the non-linear effects and interactions of process and material parameters. In this work, the ability of an artificial neural network model to predict optimum process parameters and tools geometry which allow to obtain minimum amount of springback is tested, in the case of a cylindrical deep-drawing process.

Crina, Axinte [University of Bacau, Calea Marasesti 157, 600115 Bacau (Romania)

2007-04-07T23:59:59.000Z

463

Spatially Resolved Mapping of Disorder Type and Distribution in Random Systems using Artificial Neural Network Recognition  

SciTech Connect (OSTI)

The spatial variability of the polarization dynamics in thin film ferroelectric capacitors was probed by recognition analysis of spatially-resolved spectroscopic data. Switching spectroscopy piezoresponse force microscopy was used to measure local hysteresis loops and map them on a 2D random-bond, random-field Ising model. A neural-network based recognition approach was utilized to analyze the hysteresis loops and their spatial variability. Strong variability is observed in the polarization dynamics around macroscopic cracks due to the modified local elastic and electric boundary conditions, with most pronounced effect on the length scale of ~100 nm away from the crack.

Jesse, Stephen [ORNL; Kalinin, Sergei V [ORNL; Kumar, Amit [ORNL; Ovchinnikov, Oleg S [ORNL; Guo, Senli [ORNL; Griggio, Flavio [ORNL; Trolier-Mckinstry, Susan E [ORNL

2011-01-01T23:59:59.000Z

464

A novel Artificial Neural Network training method combined with Quantum Computational Multi-Agent System theory  

Science Journals Connector (OSTI)

Artificial Neural Networks (ANNs) are powerful tools that can be used to model and investigate various complex and non-linear phenomena. In this study, we construct a new ANN, which is based on Multi-Agent System (MAS) theory and quantum computing algorithm. All nodes in this new ANN are presented as Quantum Computational (QC) agents, and these agents have learning ability. A novel ANN training method was proposed via implementing QCMAS reinforcement learning. This new ANN has powerful parallel-work ability and its training time is shorter than classic algorithm. Experiment results show that this method is effective.

Xiangping Meng; Jianzhong Wang; Yuzhen Pi; Quande Yuan

2009-01-01T23:59:59.000Z

465

Comparative evaluation of neural-network-based and PI current controllers for HVDC transmission  

SciTech Connect (OSTI)

An investigation into a neural network (NN)-based controller, composed of a NN trained off-line in parallel with a NN trained on-line, is described in this paper. This NN controller has the potential of replacing the PI controller traditionally used for HVDC transmission systems. A theoretical basis for the operational behavior of the individual NN controllers is presented. Comparisons between the responses obtained with the NN and PI controllers for the rectifier of an HVDC transmission system are made under typical system perturbations and faults.

Sood, V.K.; Kandil, N.; Patel, R.V.; Khorasani, K. (Concordia Univ., Montreal, Quebec (Canada). Dept. of Electrical and Computer Engineering)

1994-05-01T23:59:59.000Z

466

Extension of an Artificial Neural Network Algorithm for Estimating Sulfur Content of Sour Gases at Elevated Temperatures and Pressures  

Science Journals Connector (OSTI)

Extension of an Artificial Neural Network Algorithm for Estimating Sulfur Content of Sour Gases at Elevated Temperatures and Pressures ... (1, 39) The i neuron within the hidden k layer performs the following tasks: summation of the arriving weighted inputs and propagations of the resulting summation through an activation function, f, to the adjacent neurons of the next hidden layer or to the output neuron(s). ... This work deals with the potential application of artificial neural networks (ANN) to represent PVT data within their exptl. ...

Mehdi Mehrpooya; Amir H. Mohammadi; Dominique Richon

2009-11-19T23:59:59.000Z

467

Neural network system and methods for analysis of organic materials and structures using spectral data  

DOE Patents [OSTI]

Apparatus and processes are described for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

Meyer, B.J.; Sellers, J.P.; Thomsen, J.U.

1993-06-08T23:59:59.000Z

468

Consistent empirical physical formula construction for recoil energy distribution in HPGe detectors using artificial neural networks  

E-Print Network [OSTI]

The gamma-ray tracking technique is one of the highly efficient detection method in experimental nuclear structure physics. On the basis of this method, two gamma-ray tracking arrays, AGATA in Europe and GRETA in the USA, are currently being developed. The interactions of neutrons in these detectors lead to an unwanted background in the gamma-ray spectra. Thus, the interaction points of neutrons in these detectors have to be determined in the gamma-ray tracking process in order to improve photo-peak efficiencies and peak-to-total ratios of the gamma-ray peaks. Therefore, the recoil energy distributions of germanium nuclei due to inelastic scatterings of 1-5 MeV neutrons were obtained both experimentally and using artificial neural networks. Also, for highly nonlinear detector response for recoiling germanium nuclei, we have constructed consistent empirical physical formulas (EPFs) by appropriate layered feed-forward neural networks (LFNNs). These LFNN-EPFs can be used to derive further physical functions which could be relevant to determination of neutron interactions in gamma-ray tracking process.

Serkan Akkoyun; Nihat Yildiz

2012-02-16T23:59:59.000Z

469

Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks  

SciTech Connect (OSTI)

A new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between downscaled output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates that this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by downscaling multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data.

Kumar, Jitendra [ORNL] [ORNL; Brooks, Bjørn-Gustaf J. [University of Illinois, Urbana-Champaign] [University of Illinois, Urbana-Champaign; Thornton, Peter E [ORNL] [ORNL; Dietze, Michael [University of Illinois, Urbana-Champaign] [University of Illinois, Urbana-Champaign

2012-01-01T23:59:59.000Z

470

Hydroelectric power plant management relying on neural networks and expert system integration  

Science Journals Connector (OSTI)

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.

J.M. Molina; P. Isasi; A. Berlanga; A. Sanchis

2000-01-01T23:59:59.000Z

471

Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods  

Science Journals Connector (OSTI)

The study of financial markets has been addressed in many works during the last years. Different methods have been used in order to capture the non-linear behavior which is characteristic of these complex systems. The development of profitable strategies has been associated with the predictive character of the market movement, and special attention has been devoted to forecast the trends of financial markets. This work performs a predictive study of the principal index of the Brazilian stock market through artificial neural networks and the adaptive exponential smoothing method, respectively. The objective is to compare the forecasting performance of both methods on this market index, and in particular, to evaluate the accuracy of both methods to predict the sign of the market returns. Also the influence on the results of some parameters associated to both methods is studied. Our results show that both methods produce similar results regarding the prediction of the index returns. On the contrary, the neural networks outperform the adaptive exponential smoothing method in the forecasting of the market movement, with relative hit rates similar to the ones found in other developed markets.

E.L. de Faria; Marcelo P. Albuquerque; J.L. Gonzalez; J.T.P. Cavalcante; Marcio P. Albuquerque

2009-01-01T23:59:59.000Z

472

Can Artificial Neural Networks be Applied in Seismic Predicition? Preliminary Analysis Applying Radial Topology. Case: Mexico  

E-Print Network [OSTI]

Tectonic earthquakes of high magnitude can cause considerable losses in terms of human lives, economic and infrastructure, among others. According to an evaluation published by the U.S. Geological Survey, 30 is the number of earthquakes which have greatly impacted Mexico from the end of the XIX century to this one. Based upon data from the National Seismological Service, on the period between January 1, 2006 and May 1, 2013 there have occurred 5,826 earthquakes which magnitude has been greater than 4.0 degrees on the Richter magnitude scale (25.54% of the total of earthquakes registered on the national territory), being the Pacific Plate and the Cocos Plate the most important ones. This document describes the development of an Artificial Neural Network (ANN) based on the radial topology which seeks to generate a prediction with an error margin lower than 20% which can inform about the probability of a future earthquake one of the main questions is: can artificial neural networks be applied in seismic forecast...

Mota-Hernandez, Cinthya; Alvarado-Corona, Rafael

2014-01-01T23:59:59.000Z

473

Arcing fault identification using combined Gabor Transform-neural network for transmission lines  

Science Journals Connector (OSTI)

Abstract This paper presents an intelligent identification scheme for transient faults in transmission systems using Gabor Transform (GT) and Artificial Neural Network (ANN). The successful discrimination between arcing and permanent faults can be then utilized for realize a reliable operation of autoreclosure systems. The proposed algorithm employs the GT as a signal processing technique and the ANN for pattern recognition and classification processes. The use of GT is motivated by the fact that the Gabor elementary functions have distinctive an optimal localization property in the joint time and frequency domains, which leads to an optimal feature extraction. The extracted GT coefficients are used as the inputs to a three layer feedforward ANN. The generalization capabilities of neural networks together with the GT are expected to discriminate between arcing and permanent fault cases successfully. The fault behavior is simulated by ATP/EMTP where the arc model is realized using universal arc representation. Finally, the possibility of hardware implementation of the proposed scheme is visualized in order to verify its practicality and suitability for real field operation. The results show that combining of GT along with \\{ANNs\\} achieves an excellent performance to discriminate between arcing and permanent faults with eliminating the impacts of fault resistance, fault location and fault inception angle as compared with conventional discriminators.

Tamer A. Kawady; Nagy I. Elkalashy; Ayman E. Ibrahim; Abdel-Maksoud I. Taalab

2014-01-01T23:59:59.000Z

474

E-Print Network 3.0 - affects neural crest Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

induction was discovered in 1924 Summary: identified a gene that when overexpressed expanded the neural plate at the expense of adjacent neural crest... ). This was...

475

E-Print Network 3.0 - arginylation-dependent neural crest Sample...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

London Collection: Biology and Medicine 12 Hoxb1 Neural Crest Preferentially Form Glia Benjamin R. Arenkiel, Gary O. Gaufo, and Mario R. Capecchi* Summary: ARTICLE Hoxb1 Neural...

476

Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)  

Science Journals Connector (OSTI)

Vibration signals extracted from rotating parts of machineries carries lot many information with in them about the condition of the operating machine. Further processing of these raw vibration signatures measured at a convenient location of the machine ... Keywords: Artificial neural network, Bevel gear box, Fault detection, Morlet wavelet, Proximal support vector machine, Statistical features

N. Saravanan; V.N.S. Kumar Siddabattuni; K. I. Ramachandran

2010-01-01T23:59:59.000Z

477

Bowel-sound pattern analysis using wavelets and neural networks with application to long-term, unsupervised, gastrointestinal motility monitoring  

Science Journals Connector (OSTI)

This work focuses on the implementation of an autonomous system appropriate for long-term, unsupervised monitoring of bowel sounds, captured by means of abdominal surface vibrations. The autonomous intestinal motility analysis system (AIMAS) promises ... Keywords: Abdominal vibration, Bioacoustics, Bowel sounds, Multi-layer perceptron, Neural network, Pattern classification, Pattern recognition, Wavelet

C. Dimoulas; G. Kalliris; G. Papanikolaou; V. Petridis; A. Kalampakas

2008-01-01T23:59:59.000Z

478

Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding  

E-Print Network [OSTI]

for Protein Folding Richard Maclin Jude W. Shavlik Computer Sciences Dept. University of Wisconsin 1210 W learning Theory refinement Neural networks Finite-state automata Protein folding Chou-Fasman algorithm-Fasman algorithm, a method for predicting how globular proteins fold. Empirical evidence shows

Maclin, Rich

479

2286 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 55, NO. 6, JUNE 2008 Modeling, Analysis, and Neural Network Control  

E-Print Network [OSTI]

dynamics and stability when there are no differential gears. Using two in-wheel electric motors makes), induction motor, neural networks, speed estimation, traction control. I. INTRODUCTION RECENTLY, electric attractive potential. Since electric motors and inverters are utilized in drive systems, they have great

Paris-Sud XI, Université de

480

Predication Emission of an Marine Two Stroke Diesel Engine Based on Modeling of Radial Basis Function Neural Networks  

Science Journals Connector (OSTI)

As testing the marine large-scale low-speed two stroke engine to determine the engine performance map for different working conditions costs too much time and money. So the prediction of the marine engine exhaust emissions modelling is developed to define ... Keywords: emission, high pressure common rail, RBF Neural Network

Mingyv Wang; Jundong Zhang; Shaojun Zhang; Qiang Ma

2010-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

Improved one day-ahead price forecasting using combined time series and artificial neural network models for the electricity market  

Science Journals Connector (OSTI)

The price forecasts embody crucial information for generators when planning bidding strategies to maximise profits. Therefore, generation companies need accurate price forecasting tools. Comparison of neural network and auto regressive integrated moving average (ARIMA) models to forecast commodity prices in previous researches showed that the artificial neural network (ANN) forecasts were considerably more accurate than traditional ARIMA models. This paper provides an accurate and efficient tool for short-term price forecasting based on the combination of ANN and ARIMA. Firstly, input variables for ANN are determined by time series analysis. This model relates the current prices to the values of past prices. Secondly, ANN is used for one day-ahead price forecasting. A three-layered feed-forward neural network algorithm is used for forecasting next-day electricity prices. The ANN model is then trained and tested using data from electricity market of Iran. According to previous studies, in the case of neural networks and ARIMA models, historical demand data do not significantly improve predictions. The results show that the combined ANNâ??ARIMA forecasts prices with high accuracy for short-term periods. Also, it is shown that policy-making strategies would be enhanced due to increased precision and reliability.

Ali Azadeh; Seyed Farid Ghaderi; Behnaz Pourvalikhan Nokhandan; Shima Nassiri

2011-01-01T23:59:59.000Z

482

Artificial Neural Networks and quadratic Response Surfaces for the functional failure analysis of a thermal-hydraulic passive system  

E-Print Network [OSTI]

] have been widely used in reliability analysis and risk assessment [16]. Recently, advanced samplingArtificial Neural Networks and quadratic Response Surfaces for the functional failure analysis decay heat removal system of a Gas-cooled Fast Reactor (GFR). Keywords: epistemic uncertainties, passive

Paris-Sud XI, Université de

483

Estimating of the Dry Unit Weight of Compacted Soils Using General Linear Model and Multi-layer Perceptron Neural Networks  

Science Journals Connector (OSTI)

Compaction of earth fill is a very important stage of construction projects. Degree of compaction is defined by relative compaction. The relative compaction of a compacted earth fill is calculated by dividing the dry unit weight obtained from in situ ... Keywords: Dry unit weight, Earth fill, General linear model, Multi-layer perceptron neural networks, Relative compaction, Standard Proctor test

Ersin Kolay, Tugce Baser

2014-05-01T23:59:59.000Z

484

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 3, MAY 2006 771 On Algorithmic Rate-Coded AER Generation  

E-Print Network [OSTI]

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 3, MAY 2006 771 On Algorithmic Rate-Coded AER of frames into the spike event-based representation known as the address-event-rep- resentation (AER). In this paper we concentrate on rate-coded AER. The problem is addressed as an algorithmic problem, in which

Barranco, Bernabe Linares

485

Functional link artificial neural network applied to active noise control of a mixture of tonal and chaotic noise  

Science Journals Connector (OSTI)

Many practical noises emanating from rotating machines with blades generate a mixture of tonal and the chaotic noise. The tonal component is related to the rotational speed of the machine and the chaotic component is related to the interaction of the ... Keywords: Active noise control (ANC), Chaotic noise, Functional link artificial neural network (FLANN), Hybrid ANC, Narrowband ANC

Santosh Kumar Behera, Debi Prasad Das, Bidyadhar Subudhi

2014-10-01T23:59:59.000Z

486

The prediction of heating energy consumption in a model house by using artificial neural networks in Denizli-Turkey  

Science Journals Connector (OSTI)

Turkey does not have petrol and natural gas reserves on a large scale. National energy resources are lignite and hydropower. Together with increasing environmental problems and diminishing fossil resources, studies focusing on energy reduction as well ... Keywords: Artificial neural network, Degree-hour method, Energy consumption, Heating

Ö. Altan Dombayc?

2010-02-01T23:59:59.000Z

487

An integrated growing-pruning method for feedforward network training  

Science Journals Connector (OSTI)

In order to facilitate complexity optimization in feedforward networks, several algorithms are developed that combine growing and pruning. First, a growing scheme is presented which iteratively adds new hidden units to full-trained networks. Then, a ... Keywords: Back propagation, Cascade correlation, Growing, Output weight optimization-Hidden weight optimization, Pruning

Pramod L. Narasimha; Walter H. Delashmit; Michael T. Manry; Jiang Li; Francisco Maldonado

2008-08-01T23:59:59.000Z

488

Ennett CM, Frize M. An investigation into the strengths and limitations of artificial neural networks: an application to an adult ICU patient database. Proc AMIA Symp 1998:998.  

E-Print Network [OSTI]

into the Strengths and Limitations of Artificial Neural Networks: An Application to an Adult ICU Patient Database The objective was to determine the optimal operating conditions for an artificial neural network (ANNEnnett CM, Frize M. An investigation into the strengths and limitations of artificial neural

Frize, Monique

489

Artificial neural network based characterization of the volume of tissue activated during deep brain stimulation  

Science Journals Connector (OSTI)

Objective. Clinical deep brain stimulation (DBS) systems can be programmed with thousands of different stimulation parameter combinations (e.g. electrode contact(s), voltage, pulse width, frequency). Our goal was to develop novel computational tools to characterize the effects of stimulation parameter adjustment for DBS. Approach. The volume of tissue activated (VTA) represents a metric used to estimate the spatial extent of DBS for a given parameter setting. Traditional methods for calculating the VTA rely on activation function (AF)-based approaches and tend to overestimate the neural response when stimulation is applied through multiple electrode contacts. Therefore, we created a new method for VTA calculation that relied on artificial neural networks (ANNs). Main results. The ANN-based predictor provides more accurate descriptions of the spatial spread of activation compared to AF-based approaches for monopolar stimulation. In addition, the ANN was able to accurately estimate the VTA in response to multi-contact electrode configurations. Significance. The ANN-based approach may represent a useful method for fast computation of the VTA in situations with limited computational resources, such as a clinical DBS programming application on a tablet computer.

Ashutosh Chaturvedi; J Luis Luján; Cameron C McIntyre

2013-01-01T23:59:59.000Z

490

Artificial neural networks: Principle and application to model based control of drying systems -- A review  

SciTech Connect (OSTI)

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.

Thyagarajan, T.; Ponnavaikko, M. [Crescent Engineering Coll., Madras (India); Shanmugam, J. [Madras Inst. of Tech. (India); Panda, R.C.; Rao, P.G. [Central Leather Research Inst., Madras (India)

1998-07-01T23:59:59.000Z

491

RATE COEFFICIENTS FOR THE COLLISIONAL EXCITATION OF MOLECULES: ESTIMATES FROM AN ARTIFICIAL NEURAL NETWORK  

SciTech Connect (OSTI)

An artificial neural network (ANN) is investigated as a tool for estimating rate coefficients for the collisional excitation of molecules. The performance of such a tool can be evaluated by testing it on a data set of collisionally induced transitions for which rate coefficients are already known: the network is trained on a subset of that data set and tested on the remainder. Results obtained by this method are typically accurate to within a factor of approx2.1 (median value) for transitions with low excitation rates and approx1.7 for those with medium or high excitation rates, although 4% of the ANN outputs are discrepant by a factor of 10 or more. The results suggest that ANNs will be valuable in extrapolating a data set of collisional rate coefficients to include high-lying transitions that have not yet been calculated. For the asymmetric top molecules considered in this paper, the favored architecture is a cascade-correlation network that creates 16 hidden neurons during the course of training, with three input neurons to characterize the nature of the transition and one output neuron to provide the logarithm of the rate coefficient.

Neufeld, David A. [Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States)

2010-01-01T23:59:59.000Z

492

Artificial neural network modeling of geothermal district heating system thought exergy analysis  

Science Journals Connector (OSTI)

This paper deals with an artificial neural network (ANN) modeling to predict the exergy efficiency of geothermal district heating system under a broad range of operating conditions. As a case study, the Afyonkarahisar geothermal district heating system (AGDHS) in Turkey is considered. The average daily actual thermal data acquired from the AGDHS in the 2009–2010 heating season are collected and employed for exergy analysis. An ANN modeling is developed based on backpropagation learning algorithm for predicting the exergy efficiency of the system according to parameters of the system, namely the ambient temperature, flow rate and well head temperature. Then, the recorded and calculated data conducted in the AGDHS at different dates are used for training the network. The results showed that the network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values. The results confirmed that the ANN modeling can be applied successfully and can provide high accuracy and reliability for predicting the exergy performance of geothermal district heating systems.

Ali Keçeba?; ?smail Yabanova; Mehmet Yumurtac?

2012-01-01T23:59:59.000Z

493

PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 2, Code manual  

SciTech Connect (OSTI)

We recommend the reader first review Volume 1 of this document, Code Theory, before reading Volume 2. In this volume we make extensive use of terms and concepts described and defined in Volume 1 which are not redefined here to the same extent. To try to reduce the amount of redundant information, we have restricted this volume to the presentation of the expert system code and refer back to the theory described in Volume 1 when necessary. Verification and validation of the results are presented in Volume 3, Application, of this document. Volume 3 also presents the implementation of the component characteristics diagnostic approach through artificial neural networks discussed in Volume 1. We decided to present the component characteristics approach in Volume 3, as opposed to write a separate code manual for it, because the approach, although general, requires a case-by-case analysis. The purpose of this volume is to present the details of the expert system (ES) portion o the PRODIAG process diagnostic program. In addition, we present here the graphical diagnostics interface (GDI) and illustrate the combined use of the ES and GDI with a sample problem. For completeness, we provide the file names of all files, programs and major subroutines of these two systems, ES and GDI, and their corresponding location in the Reactor Analysis Division (RA) computer network and Reactor Engineering Division (RE) computer network as of 30 September 1995.

Reifman, J.; Wei, T.Y.C.

1995-09-01T23:59:59.000Z

494

Discrimination Analysis of Earthquakes and Man-Made Events Using ARMA Coefficients Determination by Artificial Neural Networks  

SciTech Connect (OSTI)

A Quadratic Neural Networks (QNNs) model has been developed for identifying seismic source classification problem at regional distances using ARMA coefficients determination by Artificial Neural Networks (ANNs). We have devised a supervised neural system to discriminate between earthquakes and chemical explosions with filter coefficients obtained by windowed P-wave phase spectra (15 s). First, we preprocess the recording's signals to cancel out instrumental and attenuation site effects and obtain a compact representation of seismic records. Second, we use a QNNs system to obtain ARMA coefficients for feature extraction in the discrimination problem. The derived coefficients are then applied to the neural system to train and classification. In this study, we explore the possibility of using single station three-component (3C) covariance matrix traces from a priori-known explosion sites (learning) for automatically recognizing subsequent explosions from the same site. The results have shown that this feature extraction gives the best classifier for seismic signals and performs significantly better than other classification methods. The events have been tested, which include 36 chemical explosions at the Semipalatinsk test site in Kazakhstan and 61 earthquakes (mb = 5.0-6.5) recorded by the Iranian National Seismic Network (INSN). The 100% correct decisions were obtained between site explosions and some of non-site events. The above approach to event discrimination is very flexible as we can combine several 3C stations.

AllamehZadeh, Mostafa, E-mail: dibaparima@yahoo.com [International Institute of Earthquake Engineering and Seismology (Iran, Islamic Republic of)

2011-12-15T23:59:59.000Z

495

Determination of elastic properties of a film-substrate system by using the neural networks  

SciTech Connect (OSTI)

An inverse method based on artificial neural network (ANN) is presented to determine the elastic properties of films from laser-genrated surface waves. The surface displacement responses are used as the inputs for the ANN model; the outputs of the ANN are the Young's modulus, density, Poisson's ratio, and thickness of the film. The finite element method is used to calculate the surface displacement responses in a film-substrate system. Levenberg Marquardt algorithm is used as numerical optimization to speed up the training process for the ANN model. In this method, the materials parameters are not recovered from the dispersion curves but rather directly from the transient surface displacement. We have also found that this procedure is very efficient for determining the materials parameters of layered systems.

Xu Baiqiang; Shen Zhonghua; Ni Xiaowu; Wang Jijun; Guan Jianfei; Lu Jian [Department of Applied Physics, Nanjing University of Science and Technology, Nanjing 210094 (China); Faculty of Science, Jiangsu University, Zhenjiang 212013 (China); Department of Applied Physics, Nanjing University of Science and Technology, Nanjing 210094 (China); Department of Applied Physics, Nanjing University of Science and Technology, Nanjing 210094 (China); Faculty of Science, Jiangsu University, Zhenjiang 212013 (China); Department of Applied Physics, Nanjing University of Science and Technology, Nanjing 210094 (China)

2004-12-20T23:59:59.000Z

496

Neural network modelling of thermal stratification in a solar DHW storage  

SciTech Connect (OSTI)

In this study an artificial neural network (ANN) model is introduced for modelling the layer temperatures in a storage tank of a solar thermal system. The model is based on the measured data of a domestic hot water system. The temperatures distribution in the storage tank divided in 8 equal parts in vertical direction were calculated every 5 min using the average 5 min data of solar radiation, ambient temperature, mass flow rate of collector loop, load and the temperature of the layers in previous time steps. The introduced ANN model consists of two parts describing the load periods and the periods between the loads. The identified model gives acceptable results inside the training interval as the average deviation was 0.22 C during the training and 0.24 C during the validation. (author)

Geczy-Vig, P.; Farkas, I. [Department of Physics and Process Control, Szent Istvan University, Pater K. u. 1., H-2103 Goedoello (Hungary)

2010-05-15T23:59:59.000Z

497

Holt’s exponential smoothing and neural network models for forecasting interval-valued time series  

Science Journals Connector (OSTI)

Interval-valued time series are interval-valued data that are collected in a chronological sequence over time. This paper introduces three approaches to forecasting interval-valued time series. The first two approaches are based on multilayer perceptron (MLP) neural networks and Holt’s exponential smoothing methods, respectively. In Holt’s method for interval-valued time series, the smoothing parameters are estimated by using techniques for non-linear optimization problems with bound constraints. The third approach is based on a hybrid methodology that combines the MLP and Holt models. The practicality of the methods is demonstrated through simulation studies and applications using real interval-valued stock market time series.

André Luis Santiago Maia; Francisco de A.T. de Carvalho

2011-01-01T23:59:59.000Z

498

Complex dynamics of a delayed discrete neural network of two nonidentical neurons  

SciTech Connect (OSTI)

In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zou [J. Nonlinear Sci. 15, 291–303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415–432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869–1878 (2013)]. We also give some numeric simulations to verify our theoretical results.

Chen, Yuanlong [Mathematics Department, GuangDong University of Finance, Guangzhou 510521 (China)] [Mathematics Department, GuangDong University of Finance, Guangzhou 510521 (China); Huang, Tingwen [Mathematics Department, Texas A and M University at Qatar, P. O. Box 23874, Doha (Qatar)] [Mathematics Department, Texas A and M University at Qatar, P. O. Box 23874, Doha (Qatar); Huang, Yu, E-mail: stshyu@mail.sysu.edu.cn [Mathematics Department, Sun Yat-Sen University, Guangzhou 510275, People's Republic China (China)] [Mathematics Department, Sun Yat-Sen University, Guangzhou 510275, People's Republic China (China)

2014-03-15T23:59:59.000Z

499

A nonlinear full model of switched reluctance motor with artificial neural network  

Science Journals Connector (OSTI)

This paper presents a novel nonlinear full model developed by using artificial neural networks (ANNs) for switched reluctance motors (SRMs). The proposed ANN based nonlinear model consists of two different models, namely forward and inverse model. The purpose of the forward model is to estimate the flux linkage and torque of the SRM as a function of stator current and rotor position. And, the purpose of the inverse model is to estimate stator current and flux linkage of the SRM as a function of torque and rotor position. Also conversions can be achieved between torque, stator current and flux linkage with these models. Computational load of the processor has been considered and minimized to use the developed model in real industrial applications. The experimental tests are realized to verify the accuracy and feasibility of the proposed model.

Oguz Ustun

2009-01-01T23:59:59.000Z

500

A fast solver for combined emission/generation allocation using a Hopfield neural network  

Science Journals Connector (OSTI)

The combined economic/emission dispatch (CEED) problem is obtained by considering both the economy and the emission objectives with required constraints. Many optimisation techniques are slow for such complex optimisation tasks and are not suitable for online use. This paper presents an optimisation algorithm for solving constrained CEED, through the application of a flexible Hopfield neural network (HNN). The constrained CEED must satisfy the system load demand and practical operation constraints of generators. The feasibility of the proposed HNN using to solve CEED is demonstrated using a three-unit test system and it is compared with the other methods in terms of solution quality and computation efficiency. The simulation results showed that the proposed HNN method was indeed capable of obtaining higher-quality solutions efficiently in CEED problems with a much shorter computation time compared with other methods.

Farid Benhamida; Belhachem Rachid; Souag Slimane; Ramdani Youcef

2013-01-01T23:59:59.000Z