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1

Estimation of subsurface temperatures in the Tattapani geothermal field, central India from limited volume of magnetotelluric data and borehole thermograms using a constructive back-propagation neural network  

Science Conference Proceedings (OSTI)

A constructive back-propagation code which was designed to run as a single hidden layer, feed-forward neural network (SLFFNN) has been adapted and used to estimate subsurface temperature from a small volume of magnetotelluric (MT) derived ...

Anthony E. Akpan; Mahesh Narayanan; T. Harinarayana

2

The Predicting of Reservoir Algae Viscosity Based on Independent Component Analysis and Back Propagation Artificial Neural Networks  

Science Conference Proceedings (OSTI)

With the rapid development of industry and agriculture, an increasing of nitrogen phosphorus and other nutrient emission has accelerated the eutrophication process and stimulated the abnormal reproduction of algae. Frequent outbreaks of algal bloom in ... Keywords: algal bloom, algae concentration prediction, independent component analysis, BP neural network, Songshan Lake reservoir

Chang Xu; Hongliang Zhou; Hongjian Zhang

2012-05-01T23:59:59.000Z

3

A genetic algorithm based back propagation network for simulation of stress-strain response of ceramic-matrix-composites  

Science Conference Proceedings (OSTI)

Ceramic-matrix-composites (CMCs) are fast replacing other materials in many applications where the higher production costs can be offset by significant improvement in performance. In applications such as cutting and forming tools, wear parts in machinery, ... Keywords: Back propagation, Ceramic-matrix-composites, Genetic algorithms, Hybrid networks, Interface elements, Neural networks

H. Sudarsana Rao; Vaishali G. Ghorpade; A. Mukherjee

2006-01-01T23:59:59.000Z

4

Estimation of formation strength index of aquifer from neural networks  

Science Conference Proceedings (OSTI)

The purpose of this study is to construct a model that predicts an aquifer's formation strength index (the ratio of shear modulus and bulk compressibility, G/C"b) from geophysical well logs by using a back-propagation neural network (BPNN). The BPNN ... Keywords: Back-propagation neural networks, Geophysical well logs, Groundwater, Soft computing

Bieng-Zih Hsieh; Chih-Wen Wang; Zsay-Shing Lin

2009-09-01T23:59:59.000Z

5

A neural network model based on the multi-stage optimization approach for short-term food price forecasting in China  

Science Conference Proceedings (OSTI)

Many studies have demonstrated that back-propagation neural network can be effectively used to uncover the nonlinearity in the financial markets. Unfortunately, back-propagation algorithm suffers the problems of slow convergence, inefficiency, and lack ... Keywords: Artificial neural network, Back-propagation, Food price forecasting, Multi-stage optimization approach, Time series forecasting

Zou Haofei; Xia Guoping; Yang Fangting; Yang Han

2007-08-01T23:59:59.000Z

6

Prediction of accumulated temperature in vegetation period using artificial neural network  

Science Conference Proceedings (OSTI)

In this paper, the theory of artificial neural network with back-propagation algorithm (BPN) is presented, and the BPN model is used to predict the accumulated temperature for Northeast China, North China, and the Huang-Huai-Hai Plain. A total of 235 ... Keywords: Accumulated temperature prediction, Artificial neural network, Back-propagation algorithm

Chunqiao Mi; Jianyu Yang; Shaoming Li; Xiaodong Zhang; Dehai Zhu

2010-06-01T23:59:59.000Z

7

Development of a predictive system for car fuel consumption using an artificial neural network  

Science Conference Proceedings (OSTI)

A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance ... Keywords: Artificial neural network, Back-propagation algorithm, Fuel consumption

Jian-Da Wu; Jun-Ching Liu

2011-05-01T23:59:59.000Z

8

Support vector machines versus back propagation algorithm for oil price prediction  

Science Conference Proceedings (OSTI)

The importance of crude oil in the world economy has made it imperative that efficient models be designed for predicting future prices. Neural networks can be used as prediction models, thus, in this paper we investigate and compare the use of a support ... Keywords: back propagation algorithm, crude oil, neural networks, price prediction, radial basis function, support vector machines

Adnan Khashman; Nnamdi I. Nwulu

2011-05-01T23:59:59.000Z

9

A BP neural network predictor model for desulfurizing molten iron  

Science Conference Proceedings (OSTI)

Desulfurization of molten iron is one of the stages of steel production process. A back-propagation (BP) artificial neural network (ANN) model is developed to predict the operation parameters for desulfurization process in this paper. The primary objective ...

Zhijun Rong; Binbin Dan; Jiangang Yi

2005-07-01T23:59:59.000Z

10

HVAC Room Temperature Prediction Control Based on Neural Network Model  

Science Conference Proceedings (OSTI)

HVAC (Heating Ventilating &Air-conditioning) system is a nonlinear complex system with delay. It is very difficult to build a mathematical model of HVAC and implement model-based control. Since a BP (Back Propagation) neural network can fully approximate ... Keywords: BP neural network, predictive control, HVAC, least squares method

Shujiang Li, Shuang Ren, Xiangdong Wang

2013-01-01T23:59:59.000Z

11

An efficient CMAC neural network for stock index forecasting  

Science Conference Proceedings (OSTI)

Stock index forecasting is one of the major activities of financial firms and private investors in making investment decisions. Although many techniques have been developed for predicting stock index, building an efficient stock index forecasting model ... Keywords: Back-propagation neural network, Cerebellar model articulation controller, Neural network, Stock index forecasting, Support vector regression

Chi-Jie Lu; Jui-Yu Wu

2011-11-01T23:59:59.000Z

12

VTG schemes for using back propagation for multivariate time series prediction  

Science Conference Proceedings (OSTI)

This research proposes the three schemes of estimating and adding mid-terms to multivariate time series. In this research, the back propagation is adopted as the approach to multivariate time series prediction. It is traditionally designed for the task ... Keywords: Multivariate time series prediction, Neural networks, Virtual terms

Taeho Jo

2013-05-01T23:59:59.000Z

13

Extraction of voltage harmonics using multi-layer perceptron neural network  

Science Conference Proceedings (OSTI)

This paper presents a harmonic extraction algorithm using artificial neural networks for Dynamic Voltage Restorers (DVRs). The suggested algorithm employs a feed forward Multi Layer Perceptron (MLP) Neural Network with error back propagation learning ... Keywords: Dynamic Voltage Restorer, Harmonic extraction, Multi Layer Perceptron, Neural Networks

Mehmet Tümay; M. Emin Meral; K. Ça?atay Bayindir

2008-09-01T23:59:59.000Z

14

Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir  

Science Conference Proceedings (OSTI)

In this work we investigate how artificial neural network (ANN) evolution with genetic algorithm (GA) improves the reliability and predictability of artificial neural network. This strategy is applied to predict permeability of Mansuri Bangestan reservoir ... Keywords: Back propagation, Genetic algorithm, Neural network, Permeability, Reservoir, Well log data

Rasoul Irani; Reza Nasimi

2011-08-01T23:59:59.000Z

15

Neural Networks  

SciTech Connect

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

16

Comparing statistical and neural network approaches for urban air pollution time series analysis  

Science Conference Proceedings (OSTI)

The paper presents an analysis of the performances obtained by using an artificial neural networks model and several statistical models for urban air quality forecasting. The time series of monthly averages concentrations (Sedimentable Dusts, Total Suspended ... Keywords: ARIMA, back-propagation, feed-forward neural network, statistical models, time series, urban air quality

Daniel Dunea; Mihaela Oprea; Emil Lungu

2008-02-01T23:59:59.000Z

17

Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting  

Science Conference Proceedings (OSTI)

This paper investigates the effectiveness of the genetic algorithm (GA) evolved neural network for rainfall-runoff forecasting and its application to predict the runoff in a catchment located in a semi-arid climate in Morocco. To predict the runoff at ... Keywords: Back propagation, Catchment, Genetic algorithm, Neural network, Rainfall-runoff, Semi-arid climate

A. Sedki; D. Ouazar; E. El Mazoudi

2009-04-01T23:59:59.000Z

18

Research on Monitoring System of Circuit Breakers Based on Neural Networks  

Science Conference Proceedings (OSTI)

The paper proposed a monitoring system for the circuit breakers in the substation based on the Back Propagation Neural Networks(BPNN). The novel temperature and humidity sensor was used in the system to get temperature and humidity value in the breakers. ... Keywords: Circuit Breakers, Monitoring System, Neural Networks, Malfunction Diagnosis

Yimin Hou; Tao Liu; Xiangmin Lun; Jianjun Lan; Yang Cui

2010-04-01T23:59:59.000Z

19

Artificial neural network based prediction of drill flank wear from motor current signals  

Science Conference Proceedings (OSTI)

In this work, a multilayer neural network with back propagation algorithm (BPNN) has been applied to predict the average flank wear of a high speed steel (HSS) drill bit for drilling on a mild steel work piece. Root mean square (RMS) value of the spindle ... Keywords: Artificial neural network, Current sensors, Drilling, Flank wear, Regression model

Karali Patra; Surjya K. Pal; Kingshook Bhattacharyya

2007-06-01T23:59:59.000Z

20

QSAR modeling of CCR5 receptor antagonists using artificial neural network  

Science Conference Proceedings (OSTI)

In-silico prediction methods are gaining the popularity in drug discovery processes as they are relatively inexpensive and less time consuming. In this study, Artificial Neural Network (ANN) based on back propagation algorithm (BP algorithm) has been ... Keywords: CCR5, QSAR, artificial intelligence, neural networks

Yogesh D. Aher; Prabha Garg

2007-02-01T23:59:59.000Z

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.


21

The Direct Neural Control Applied to the Position Control in Hydraulic Servo System  

Science Conference Proceedings (OSTI)

This study utilizes the direct neural control (DNC) based on back propagation neural networks (BPN) with specialized learning architecture applied to control the position of a cylinder rod in an electro-hydraulic servo system (EHSS). The proposed neural ... Keywords: Back propagation, Electro-hydraulic servo system, Neural networks, Position control

Yuan Kang; Yi-Wei Chen; Yeon-Pun Chang; Ming-Huei Chu

2008-09-01T23:59:59.000Z

22

Computationally Efficient Neural Network Intrusion Security Awareness  

SciTech Connect

An enhanced version of an algorithm to provide anomaly based intrusion detection alerts for cyber security state awareness is detailed. A unique aspect is the training of an error back-propagation neural network with intrusion detection rule features to provide a recognition basis. Network packet details are subsequently provided to the trained network to produce a classification. This leverages rule knowledge sets to produce classifications for anomaly based systems. Several test cases executed on ICMP protocol revealed a 60% identification rate of true positives. This rate matched the previous work, but 70% less memory was used and the run time was reduced to less than 1 second from 37 seconds.

Todd Vollmer; Milos Manic

2009-08-01T23:59:59.000Z

23

Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms  

Science Conference Proceedings (OSTI)

The process modeling for the growth rate in pulsed laser deposition (PLD)-grown ZnO thin films was investigated using neural networks (NNets) based on the back-propagation (BP) algorithm and the process recipes was optimized via genetic algorithms (GAs). ... Keywords: Genetic algorithms, Neural networks, PLD, Process modeling, ZnO

Young-Don Ko; Pyung Moon; Chang Eun Kim; Moon-Ho Ham; Jae-Min Myoung; Ilgu Yun

2009-03-01T23:59:59.000Z

24

Electricity price short-term forecasting using artificial neural networks  

Science Conference Proceedings (OSTI)

This paper presents the System Marginal Price (SMP) short-term forecasting implementation using the Artificial Neural Networks (ANN) computing technique. The described approach uses the three-layered ANN paradigm with back-propagation. The retrospective SMP real-world data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The results presented in this paper confirm considerable value of the ANN based approach in forecasting the SMP.

Szkuta, B.R.; Sanabria, L.A.; Dillon, T.S. [La Trobe Univ., Melbourne (Australia). Applied Computing Research Inst.

1999-08-01T23:59:59.000Z

25

A combined neural network and DEA for measuring efficiency of large scale datasets  

Science Conference Proceedings (OSTI)

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms ... Keywords: Back-propagation DEA, Data Envelopment Analysis, Large datasets, Neural networks

Ali Emrouznejad; Estelle Shale

2009-02-01T23:59:59.000Z

26

BP neural networks combined with PLS applied to pattern recognition of Vis/NIRs  

Science Conference Proceedings (OSTI)

Vis/NIRs technique can be used in non-destructive measurement of the material internal quality in many fields. In this study, a mixed algorithm combined with back-propagation neural networks (BPNNs) and partial least squares (PLS) method was applied ...

Di Wu; Yong He; Yongni Shao; Shuijuan Feng

2006-09-01T23:59:59.000Z

27

Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion  

Science Conference Proceedings (OSTI)

The deformation behavior of type 304L stainless steel during hot torsion is investigated using artificial neural network (ANN). Torsion tests in the temperature range of 600-1200^oC and in the (maximum surface) strain rate range of 0.1-100s^-^1 were ... Keywords: Artificial neural network, Austenitic stainless steel, Back propagation, Deformation behavior, Hot torsion, Resilient propagation, Sensitivity

Sumantra Mandal; P. V. Sivaprasad; S. Venugopal; K. P. N. Murthy

2009-01-01T23:59:59.000Z

28

Application of neural networks to waste site screening  

Science Conference Proceedings (OSTI)

Waste site screening requires knowledge of the actual concentrations of hazardous materials and rates of flow around and below the site with time. The present approach consists primarily of drilling boreholes near contaminated sites and chemically analyzing the extracted physical samples and processing the data. This is expensive and time consuming. The feasibility of using neural network techniques to reduce the cost of waste site screening was investigated. Two neural network techniques, gradient descent back propagation and fully recurrent back propagation were utilized. The networks were trained with data received from Westinghouse Hanford Corporation. The results indicate that the network trained with the fully recurrent technique shows satisfactory generalization capability. The predicted results are close to the results obtained from a mathematical flow prediction model. It is possible to develop a new tool to predict the waste plume, thus substantially reducing the number of the bore sites and samplings. There are a variety of applications for this technique in environmental site screening and remediation. One of the obvious applications would be for optimum well siting. A neural network trained from the existing sampling data could be utilized to decide where would be the best position for the next bore site. Other applications are discussed in the report.

Dabiri, A.E.; Garrett, M.; Kraft, T.; Hilton, J.; VanHammersveld, M.

1993-02-01T23:59:59.000Z

29

Legacy - Neural Networks  

Science Conference Proceedings (OSTI)

... "Massively Parallel Implementation of Neural Network Architectures," In Proceedings of the SPIE, volume 1452, pages 532-543, Feb. 25 - Mar. ...

2011-05-12T23:59:59.000Z

30

Set separation Neural Network paradigms  

E-Print Network (OSTI)

for forecasting financial time series 29 f´evrier 2008 Designing a neural network for forecasting financial time for forecasting financial time series #12;Neural Net The inputs Set separation Neural Network paradigms From network for forecasting financial time series #12;Neural Net The inputs Set separation Neural Network

Chen, Yiling

31

Evolving neural networks  

Science Conference Proceedings (OSTI)

Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially ... Keywords: evolutionary computation, hyperneat, neat, neural networks, neuroevolution

Kenneth O. Stanley

2012-07-01T23:59:59.000Z

32

Neural networks in astronomy  

Science Conference Proceedings (OSTI)

In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform ... Keywords: Bayesian learning, MLP, MUSIC, PCA, astronomy, data mining, neural networks, self-organizing maps

Roberto Tagliaferri; Giuseppe Longo; Leopoldo Milano; Fausto Acernese; Fabrizio Barone; Angelo Ciaramella; Rosario De Rosa; Ciro Donalek; Antonio Eleuteri; Giancarlo Raiconi; Salvatore Sessa; Antonino Staiano; Alfredo Volpicelli

2003-04-01T23:59:59.000Z

33

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.

34

Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process  

Science Conference Proceedings (OSTI)

Monitoring and control of multiple process quality characteristics (responses) in grinding plays a critical role in precision parts manufacturing industries. Precise and accurate mathematical modelling of multiple response process behaviour holds the ... Keywords: Back propagation neural network, Gradient descent algorithm, Levenberg-Marquardt algorithm, Multiple response, Quasi-Newton algorithm

Indrajit Mukherjee; Srikanta Routroy

2012-02-01T23:59:59.000Z

35

Artificial neural network simulation of battery performance  

SciTech Connect

Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, the authors have begun development of a non-phenomenological model for battery systems based on artificial neural networks. Both recurrent and non-recurrent forms of these networks have been successfully used to develop accurate representations of battery behavior. The connectionist normalized linear spline (CMLS) network has been implemented with a self-organizing layer to model a battery system with the generalized radial basis function net. Concurrently, efforts are under way to use the feedforward back propagation network to map the {open_quotes}state{close_quotes} of a battery system. Because of the complexity of battery systems, accurate representation of the input and output parameters has proven to be very important. This paper describes these initial feasibility studies as well as the current models and makes comparisons between predicted and actual performance.

O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G.; Paez, T.L.

1998-12-31T23:59:59.000Z

36

Ozone Modeling Using Neural Networks  

Science Conference Proceedings (OSTI)

Ozone models for the city of Tulsa were developed using neural network modeling techniques. The neural models were developed using meteorological data from the Oklahoma Mesonet and ozone, nitric oxide, and nitrogen dioxide (NO2) data from ...

Ramesh Narasimhan; Joleen Keller; Ganesh Subramaniam; Eric Raasch; Brandon Croley; Kathleen Duncan; William T. Potter

2000-03-01T23:59:59.000Z

37

Neural Networks for Postprocessing Model Output: ARPS  

Science Conference Proceedings (OSTI)

The temperature forecasts of the Advanced Regional Prediction System are postprocessed by a neural network. Specifically, 31 stations are considered, and for each a neural network is developed. The nine input variables to the neural network are ...

Caren Marzban

2003-06-01T23:59:59.000Z

38

Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods  

Science Conference Proceedings (OSTI)

The primary purpose of the current research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination data. Specifically, data from eddy current inspection of heat exchanger tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of ORNL. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. A PC-based data preprocessing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an effective technique for automation of diagnostics using nondestructive examination methods.

Upadhyaya, B.R.; Yan, W. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering

1993-11-01T23:59:59.000Z

39

Prototyping Neural Networks Learn Lyme Borreliosis  

Science Conference Proceedings (OSTI)

Abstract: In this paper, the application of neural network algorithms to the study of Lyme borreliosis is addressed. Three different methods are studied: self organizing maps, neural gas networks and a new approach currently under development called ... Keywords: Lyme borreliosis, analysis tools, backpropagation, circular backpropagation, feedforward neural models, knowledge representation, medical application, medical computing, neural gas networks, neural networks, self organizing maps, self-organising feature maps

S. Rovetta; R. Zunino; L. Buffrini; G. Rovetta

1995-06-01T23:59:59.000Z

40

Application of neural network on solid boronizing  

Science Conference Proceedings (OSTI)

This paper discusses an application of neural network system on the performance prediction of solid boronizing. To build the mathematics model between the solid boronizing and the prediction of boronizing performance, a neural network approach is adopted. ... Keywords: mathematics model, neural network, solid boronizing

YuXi Liu; ZhiFeng Zhang

2011-08-01T23:59:59.000Z

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

Time series forecasting with Qubit Neural Networks  

Science Conference Proceedings (OSTI)

This paper proposes a quantum learning scheme approach for time series forecasting, through the application of the new non-standard Qubit Neural Network (QNN) model. The QNN description was adapted in this work in order to resemble classical Artificial ... Keywords: artificial intelligence, artificial neural networks, quantum computing, qubit neural networks, time series forecasting

Carlos R. B. Azevedo; Tiago A. E. Ferreira

2007-08-01T23:59:59.000Z

42

Environmental Noise Source Classification Using Neural Networks  

Science Conference Proceedings (OSTI)

Neural networks have been applied to many interesting problems in different areas including noise identification/recognition. With this study, we studied noise classification using artificial neural networks (ANN). Three commonly encountered non-stationary ... Keywords: ACF-based feature parameter, environmental noise classification, Neural Networks (ANN)

Buket D. Barkana; Inci Saricicek

2010-04-01T23:59:59.000Z

43

Neural network based system for equipment surveillance  

DOE Patents (OSTI)

A method and system are disclosed 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. 33 figs.

Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

1998-04-28T23:59:59.000Z

44

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

45

Blasting Vibration Forecast Base on Neural Network  

Science Conference Proceedings (OSTI)

The influence of blasting vibration to surroundings around the blasting area can not be ignored, in order to guarantee the safety of surroundings around blasting area, blasting vibration forecasting model based on neural network is established by improved ... Keywords: Blasting vibration, Neural network, Forecast

Haiwang Ye; Fang Liu; Jian Chang; Lin Feng; Yang Wang; Peng Yao; Kai Wu

2010-10-01T23:59:59.000Z

46

Neural network characterization of scanning electron microscopy  

Science Conference Proceedings (OSTI)

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of processed film surfaces. In this study, a prediction model of scanning electron microscopy was constructed by using a generalized regression neural network ... Keywords: generalized regression neural network, genetic algorithm, model, scanning electron microscope, statistical experiment

Sanghee Kwon; Donghwan Kim; Byungwhan Kim

2008-07-01T23:59:59.000Z

47

Quantum neural networks Alexandr A. Ezhov1  

E-Print Network (OSTI)

Quantum neural networks Alexandr A. Ezhov1 and Dan Ventura2 1 Department of Mathematics, Troitsk outlines the research, development and perspectives of quantum neural networks ­ a burgeoning new field which integrates classical neurocomputing with quantum computation [1]. It is argued that the study

Martinez, Tony R.

48

Optimization neural network for solving flow problems  

Science Conference Proceedings (OSTI)

This paper describes a neural network for solving flow problems, which are of interest in many areas of application as in fuel, hydro, and electric power scheduling. The neural network consist of two layers: a hidden layer and an output layer. The hidden ...

R. Perfetti

1995-09-01T23:59:59.000Z

49

Tampa Electric Neural Network Sootblowing  

Science Conference Proceedings (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

50

Tampa Electric Neural Network Sootblowing  

Science Conference Proceedings (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. NO{sub x} 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 soot-blowing 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

2003-12-31T23:59:59.000Z

51

Tampa Electric Neural Network Sootblowing  

Science Conference Proceedings (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 co-funding (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

2004-03-31T23:59:59.000Z

52

Imbibition well stimulation via neural network design  

SciTech Connect

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

53

On Stability of Cellular Neural Networks  

Science Conference Proceedings (OSTI)

The main results about stability of cellular neural networks (CNNs) are reviewed. Some of them are extended and reformulated, with the purpose of providing to the CNN designer simple criteria for checking the stability properties. A particular emphasis ...

Pier Paolo-Civalleri; Marco Gilli

1999-11-01T23:59:59.000Z

54

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

Science Conference Proceedings (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

55

Application of Polynomial Neural Networks to Exchange Rate Forecasting  

Science Conference Proceedings (OSTI)

This research investigates the use of Ridge Polynomial Neural Network (RPNN) as non-linear prediction model to forecast the future trends of financial time series. The network was used for the prediction of one step ahead and five steps ahead of two ... Keywords: Dynamic Ridge Polynomial Neural Network, Financial time series, Multilayer Perceptron, Ridge Polynomial Neural Network

R. Ghazali; A. J. Hussain; M. N. Mohd. Salleh

2008-11-01T23:59:59.000Z

56

Using Neural Networks to Estimate Wind Turbine  

E-Print Network (OSTI)

This paper uses data collected at Central and South West Services Fort Davis wind farm to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes---lower-than-expected wind power may be an early indicator of a need for maintenance. In this paper, characteristics of wind power generation are first evaluated in order to establish the relative importance for the neural network. A four input neural network is developed and its performance is shown to be superior to the single parameter traditional model approach.

Power Generation Shuhui; Shuhui Li; Donald C. Wunsch; Edgar A. O’hair; Michael G. Giesselmann; Senior Member; Senior Member

2001-01-01T23:59:59.000Z

57

Optimal Training Sequences for Locally Recurrent Neural Networks  

Science Conference Proceedings (OSTI)

The problem of determining an optimal training schedule for a locally recurrent neural network is discussed. Specifically, the proper choice of the most informative measurement data guaranteeing the reliable prediction of the neural network response ...

Krzysztof Patan; Maciej Patan

2009-09-01T23:59:59.000Z

58

Finite time convergent learning law for continuous neural networks  

Science Conference Proceedings (OSTI)

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary ... Keywords: Discontinuous systems, Learning algorithms, Lyapunov method, Neural networks, Super-twisting

Isaac Chairez

2014-02-01T23:59:59.000Z

59

A cooperative constructive method for neural networks for pattern recognition  

Science Conference Proceedings (OSTI)

In this paper, we propose a new constructive method, based on cooperative coevolution, for designing automatically the structure of a neural network for classification. Our approach is based on a modular construction of the neural network by means of ... Keywords: Constructive algorithms, Cooperative coevolution, Evolutionary computation, Neural networks, Pattern classification

Nicolás García-Pedrajas; Domingo Ortiz-Boyer

2007-01-01T23:59:59.000Z

60

Permanent oscillations in a 3-node recurrent neural network model  

Science Conference Proceedings (OSTI)

In this paper we discuss the existence of oscillations in a specific recurrent neural network: the 3-node network with two weight parameters and one time delay. Simple and practical criteria for fixing the range of the parameters in this network model ... Keywords: 3-node networks, Recurrent neural networks, Time delays

Chunhua Feng; Christian O'Reilly; Réjean Plamondon

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.


61

Application of neural networks to measurement of temperature sensor response time  

SciTech Connect

One of the important components in nuclear reactor safety systems is the temperature measurement systems. The time response characteristics of the resistance temperature detector (RTD), the type of sensor used in PWR safety systems, is commonly represented by the time constant, which is defined as the time required to achieve 63.2% of steady-state value following a unit step change in the input. The time constant of an RTD can be measured in a laboratory by a plunge test. The results of the plunge test performed in a laboratory may not reflect the time constant of an RTD installed in a nuclear power plant. An in situ testing method called the loop current step response (LCSR) test can be applied to measure the response transients of RTDs installed in a nuclear power plant. In an LCSR test, heat is generated by passing a current through the sensing wire. A transformation has been developed to achieve the desired time constant from the LCSR response transient. However, this transformation involves complicated computation, highly trained personnel, and specialized equipment to obtain the time constant of the RTD. Because of these difficulties, a back-propagation neural network has been developed to predict the time constant from LCSR response transients.

Cahyono, A.; Katz, E.M.; Kerlin, T.W. (Univ. of Tennessee, Knoxville (United States))

1991-01-01T23:59:59.000Z

62

Analysis on Uranic Slope Stability Based on Neural Network  

Science Conference Proceedings (OSTI)

How to accurately predict the occurrence of landslides, and it has become one of the troubles in the mining process. The author made a brief introduction of artificial neural network and BP network model in this paper, and also analysis some important ... Keywords: Uranic slope, neural network, Forecast network model, safety of slope

Yufeng Zhu; Xiaoli Ding; Zhiwei Li; Shijian Zhou

2010-07-01T23:59:59.000Z

63

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

Science Conference Proceedings (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

64

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

Science Conference Proceedings (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

65

Experimental demonstration of associative memory with memristive neural networks  

Science Conference Proceedings (OSTI)

Synapses are essential elements for computation and information storage in both real and artificial neural systems. An artificial synapse needs to remember its past dynamical history, store a continuous set of states, and be ''plastic'' according to ... Keywords: Memory, Neural network hardware, Neural networks, Resistance

Yuriy V. Pershin; Massimiliano Di Ventra

2010-09-01T23:59:59.000Z

66

Restoring images with a multiscale neural network based technique  

Science Conference Proceedings (OSTI)

This paper describes a neural network based multiscale image restoration approach in which multilayer perceptrons are trained with artificial images of degraded gray level cocentered circles. The main objective of this approach is to make the neural ... Keywords: artificial neural network, image restoration, multiscale

Ana Paula Abrantes de Castro; José Demisio Simões da Silva

2008-03-01T23:59:59.000Z

67

Towards Robustness in Neural Network Based Fault Diagnosis  

Science Conference Proceedings (OSTI)

Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural ... Keywords: Dynamic Neural Network, Fault Diagnosis, Gmdh Neural Network, Robustness

Krzysztof Patan; Marcin Witczak; JóZef Korbicz

2008-12-01T23:59:59.000Z

68

Combining Belief Networks and Neural Networks for Scene Segmentation  

Science Conference Proceedings (OSTI)

We are concerned with the problem of image segmentation, in which each pixel is assigned to one of a predefined finite number of labels. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model ... Keywords: tree-structured belief network (TSBN), hierarchical modeling, Markov random field (MRF), neural network, scaled-likelihood method, conditional maximum-likelihood training, Gaussian mixture model, expectation-maximization (EM)

X. Feng; C. K. I. Williams; S. N. Felderhof

2002-04-01T23:59:59.000Z

69

On analog implementations of discrete neural networks  

Science Conference Proceedings (OSTI)

The paper will show that in order to obtain minimum size neural networks (i.e., size-optimal) for implementing any Boolean function, the nonlinear activation function of the neutrons has to be the identity function. The authors shall shortly present many results dealing with the approximation capabilities of neural networks, and detail several bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov`s superpositions they will show that implementing Boolean functions can be done using neurons having an identity nonlinear function. It follows that size-optimal solutions can be obtained only using analog circuitry. Conclusions, and several comments on the required precision are ending the paper.

Beiu, V.; Moore, K.R.

1998-12-01T23:59:59.000Z

70

Neural Network Prediction of solar cycle 24  

E-Print Network (OSTI)

The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. The level of solar activity is usually expressed by international sunspot number ($R_z$). Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. In this paper, we predict a solar index ($R_z$) in solar cycle 24 by using the neural network method. The neural network technique is used to analyze the time series of solar activity. According to our predictions of yearly sunspot number, the maximum of cycle 24 will occur in the year 2013 and will have an annual mean sunspot number of 65. Finally, we discuss our results in order to compare it with other suggested predictions.

Ajabshirizadeh, A; Abbassi, S

2010-01-01T23:59:59.000Z

71

Artificial neural network cardiopulmonary modeling and diagnosis  

DOE Patents (OSTI)

The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

Kangas, Lars J. (Richland, WA); Keller, Paul E. (Richland, WA)

1997-01-01T23:59:59.000Z

72

Artificial neural network cardiopulmonary modeling and diagnosis  

DOE Patents (OSTI)

The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

Kangas, L.J.; Keller, P.E.

1997-10-28T23:59:59.000Z

73

Fuzzy wavelet neural network for prediction of electricity consumption  

Science Conference Proceedings (OSTI)

The development of a fuzzy wavelet neural network (FWNN) for the prediction of electricity consumption is presented. The fuzzy rules that contain wavelets are constructed. Based on these rules, the structure of FWNN-based system is described. The FWNN ... Keywords: Fuzzy Wavelet Neural Network, Neurofuzzy Modeling, Prediction of Electricity Consumption, Time Series Prediction, Wavelet Network

Rahib h. Abiyev

2009-05-01T23:59:59.000Z

74

Auto claim fraud detection using Bayesian learning neural networks  

Science Conference Proceedings (OSTI)

This article explores the explicative capabilities of neural network classifiers with automatic relevance determination weight regularization, and reports the findings from applying these networks for personal injury protection automobile insurance claim ... Keywords: Automobile insurance, Bayesian learning, C45, Claim fraud, Evidence framework, IB40, Neural network

S. Viaene; G. Dedene; R. A. Derrig

2005-10-01T23:59:59.000Z

75

Design automation of cellular neural networks for data fusion applications  

Science Conference Proceedings (OSTI)

In this study, a novel methodology for the design automation of cellular neural networks (CNNs) for different applications is proposed. In particular, an evolvable algorithm has been developed providing the ability to generate the netlist of the requested ... Keywords: Cellular neural networks, Data fusion, Design automation, Sensor network

Prodromos Chatziagorakis; Georgios Ch. Sirakoulis; John N. Lygouras

2012-02-01T23:59:59.000Z

76

Analysis of Heart Diseases Dataset using Neural Network Approach  

E-Print Network (OSTI)

One of the important techniques of Data mining is Classification. Many real world problems in various fields such as business, science, industry and medicine can be solved by using classification approach. Neural Networks have emerged as an important tool for classification. The advantages of Neural Networks helps for efficient classification of given data. In this study a Heart diseases dataset is analyzed using Neural Network approach. To increase the efficiency of the classification process parallel approach is also adopted in the training phase.

Rani, K Usha

2011-01-01T23:59:59.000Z

77

Point-Wise Confidence Interval Estimation by Neural Networks: A ...  

Science Conference Proceedings (OSTI)

As the human errors cannot be accurately captured (or corrected) by a neural network, it is considered that the values in the map are reasonably accurate and  ...

78

Condition based management of gas turbine engine using neural networks.  

E-Print Network (OSTI)

??This research work is focused on the development of the hybrid neural network model to asses the gas turbine’s compressor health. Effects of various gas… (more)

Muthukumar, Krishnan.

2008-01-01T23:59:59.000Z

79

Using Neural Networks in the Maintenance and Operations REcommender...  

NLE Websites -- All DOE Office Websites (Extended Search)

using Neural Networks. By integrating the information from Computerized Maintenance Management Systems (CMMS) and Energy Managementand Control System(EMCS), MORE can analyze and...

80

Development of a Neural Network Simulator for the Constitutive ...  

Science Conference Proceedings (OSTI)

Experts in several different fields have collaborated to develop a neural network simulator graphical user interface, optimized to train and simulate constitutive ...

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

Neural node network and model, and method of teaching same  

DOE Patents (OSTI)

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.

Parlos, Alexander G. (College Station, TX); Atiya, Amir F. (College Station, TX); Fernandez, Benito (Austin, TX); Tsai, Wei K. (Irvine, CA); Chong, Kil T. (College Station, TX)

1995-01-01T23:59:59.000Z

82

Neural node network and model, and method of teaching same  

DOE Patents (OSTI)

The present invention is a fully connected feed forward network that includes at least one hidden layer. The hidden layer includes nodes in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device occurring in the feedback path (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit from all the other nodes within the same layer. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing. 21 figs.

Parlos, A.G.; Atiya, A.F.; Fernandez, B.; Tsai, W.K.; Chong, K.T.

1995-12-26T23:59:59.000Z

83

Identifying Objects in Procedural Programs Using Clustering Neural Networks  

Science Conference Proceedings (OSTI)

This paper presents a general approach for the identification of objects in procedural programs. The approach is based on neural architectures that perform an unsupervised learning of clusters. We describe two such neural architectures, explain how to ... Keywords: abstract data types, clustering, neural networks, objects

Salwa K. Abd-El-Hafiz

2000-07-01T23:59:59.000Z

84

Nuclear mass systematics using neural networks  

E-Print Network (OSTI)

New global statistical models of nuclidic (atomic) masses based on multilayered feedforward networks are developed. One goal of such studies is to determine how well the existing data, and only the data, determines the mapping from the proton and neutron numbers to the mass of the nuclear ground state. Another is to provide reliable predictive models that can be used to forecast mass values away from the valley of stability. Our study focuses mainly on the former goal and achieves substantial improvement over previous neural-network models of the mass table by using improved schemes for coding and training. The results suggest that with further development this approach may provide a valuable alternative to conventional global models.

Athanassopoulos, S; Gernoth, K A; Clark, J W

2003-01-01T23:59:59.000Z

85

Nuclear mass systematics using neural networks  

E-Print Network (OSTI)

New global statistical models of nuclidic (atomic) masses based on multilayered feedforward networks are developed. One goal of such studies is to determine how well the existing data, and only the data, determines the mapping from the proton and neutron numbers to the mass of the nuclear ground state. Another is to provide reliable predictive models that can be used to forecast mass values away from the valley of stability. Our study focuses mainly on the former goal and achieves substantial improvement over previous neural-network models of the mass table by using improved schemes for coding and training. The results suggest that with further development this approach may provide a valuable complement to conventional global models.

S. Athanassopoulos; E. Mavrommatis; K. A. Gernoth; J. W. Clark

2003-07-31T23:59:59.000Z

86

Fault Diagnosis of Transformer Based on Probabilistic Neural Network  

Science Conference Proceedings (OSTI)

In order to improve the correct rate of transformer fault diagnosis based on three-ratio method of traditional dissolved gas analysis (DGA), a novel intelligent transformer fault diagnosis method based on both DGA and probabilistic neural network (PNN) ... Keywords: transformer fault diagnosis, probabilistic neural network (PNN), improved three-ratio method

Li Song; Li Xiu-ying; Wang Wen-xu

2011-03-01T23:59:59.000Z

87

Modeling of tomato drying using artificial neural network  

Science Conference Proceedings (OSTI)

This study involves experimental works on drying of tomatoes in a tray dryer covering different variables like power of heater and air flow velocity. The data are modeled using artificial neural network and empirical mathematical equations. The results ... Keywords: Artificial neural network, Curve fitting, Drying, Mathematical model

Kamyar Movagharnejad; Maryam Nikzad

2007-11-01T23:59:59.000Z

88

Electric Load Prediction Using a Bilinear Recurrent Neural Network  

Science Conference Proceedings (OSTI)

A prediction scheme of electric load using a Bilinear Recurrent Neural Network (BRNN) is proposed in this paper. Since the BRNN is based on the bilinear polynomial, BRNN has been successfully used in modeling highly nonlinear systems with time-series ... Keywords: recurrent, neural network, forecasting, electricity

Jae-Young Kim; Dong-Chul Park; Dong-Min Woo

2010-03-01T23:59:59.000Z

89

Artificial neural networks for electricity consumption forecasting considering climatic factors  

Science Conference Proceedings (OSTI)

This work develops Artificial Neural Networks (ANN) models applied to predict the consumption forecasting considering climatic factors. It is intended to verify the influence of climatic factors on the electricity consumption forecasting through the ... Keywords: artificial neural networks, electricity consumption forecasting

Francisco David Moya Chaves

2010-06-01T23:59:59.000Z

90

Application of BP Neural Network for Wind Turbines  

Science Conference Proceedings (OSTI)

n order to solve non-linear, parameter time changing, anti-disturb and time-lag problems, a PID control method based on the BP neural network has been presented and applied in the variable pitch wind turbine control system. After analyzing the BP neural ... Keywords: BP, Control, Network, Pitch, simulink

Zuoxia Xing; Qinwei Li; Xianbin Su; Hengyi Guo

2009-10-01T23:59:59.000Z

91

Prediction of Breast Cancer Using Artificial Neural Networks  

Science Conference Proceedings (OSTI)

In this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on ... Keywords: Artificial neural network, BI-RADS, Breast cancer, Breast cancer prediction

Ismail Saritas

2012-10-01T23:59:59.000Z

92

Predicting water saturation using artificial neural networks (ANNS)  

Science Conference Proceedings (OSTI)

The application of artificial neural networks (ANNs) in the petroleum industry is widely increasing after major developments in ANN design. In this study, ANNs were used to develop a model for predicting water saturation in shaly formations using wireline ... Keywords: artificial neural networks, petroleum industry, shaly sandstone formation, water saturation

Nabil Al-Bulushi; Mariela Araujo; Martin Kraaijveld

2007-02-01T23:59:59.000Z

93

Anaerobic Digestion Process Identification Using Recurrent Neural Network Model  

Science Conference Proceedings (OSTI)

This paper proposes the use of a Recurrent Neural Network Model (RNNM) for decentralized and centralized identification of an aerobic digestion process, carried out in a fixed bed and a recirculation tank anaerobic wastewater treatment system. The analytical ... Keywords: Recurrent neural network model, backpropagation learning, decentralized model, centralized model, system identification, anaerobic digestion bioprocess

Rosalba Galvan-Guerra; Ieroham S. Baruch

2007-11-01T23:59:59.000Z

94

2011 Special Issue: Reliable prediction intervals with regression neural networks  

Science Conference Proceedings (OSTI)

This paper proposes an extension to conventional regression neural networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, ... Keywords: Confidence measures, Conformal Prediction, Neural networks, Prediction intervals, Regression, Total Electron Content

Harris Papadopoulos; Haris Haralambous

2011-10-01T23:59:59.000Z

95

Short-term streamflow forecasting: ARIMA vs neural networks  

Science Conference Proceedings (OSTI)

Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of ... Keywords: artificial neural networks, auto regressive integrated moving average, forecasting, streamflow

Juan Frausto-Solis; Esmeralda Pita; Javier Lagunas

2008-03-01T23:59:59.000Z

96

The predictions of optoelectronic attributes of LED by neural network  

Science Conference Proceedings (OSTI)

In this paper, the predictions of optoelectronic attributes of Light-Emitting Diode (LED) chip, including luminous intensity, wavelength and forward voltage by using neural network were presented. The simulated data was measured by Electrical Luminescence ... Keywords: Neural network, Optoelectronic attributes, Prediction

Pin-Hsuan Weng; Yu-Ju Chen; Shuming T. Wang; Rey-Chue Hwang

2010-09-01T23:59:59.000Z

97

A neural network-based multi-agent classifier system  

Science Conference Proceedings (OSTI)

In this paper, we propose a neural network (NN)-based multi-agent classifier system (MACS) using the trust, negotiation, and communication (TNC) reasoning model. The main contribution of this work is that a novel trust measurement method, based on the ... Keywords: Multi-agent systems, Neural networks, Pattern classification

Anas Quteishat; Chee Peng Lim; Jeffrey Tweedale; Lakhmi C. Jain

2009-03-01T23:59:59.000Z

98

POFGEC: growing neural network of classifying potential function generators  

Science Conference Proceedings (OSTI)

In this paper, we propose an architecture and learning algorithm for a growing neural network. Drawing inspiration from the idea of electrical potentials, we develop a classifier based on a set of synthesised potential fields over the domain of input ... Keywords: classification rules, electrical potentials, kernels, neural networks, potential function generators, potential functions

Natacha Gueorguieva; Iren Valova; Georgi Georgiev

2010-08-01T23:59:59.000Z

99

A Hopfield Neural Network for Image Change Detection  

Science Conference Proceedings (OSTI)

This paper outlines an optimization relaxation approach based on the analog Hopfield neural network (HNN) for solving the image change detection problem between two images. A difference image is obtained by subtracting pixel by pixel both images. The ... Keywords: Change detection, Hopfield neural network (HNN), difference images, energy minimization

G. Pajares

2006-09-01T23:59:59.000Z

100

Use of Autoassociative Neural Networks for Signal Validation  

Science Conference Proceedings (OSTI)

Recently, the use of Autoassociative Neural Networks (AANNs) to perform on-line calibration monitoring of process sensors has been shown to not only be feasible, but practical as well. This paper summarizes the results of applying AANNs to instrument ... Keywords: fault detection and isolation, neural networks, sensor calibration

J. Wesley Hines; Robert E. Uhrig; Darryl J. Wrest

1998-02-01T23:59:59.000Z

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

Using Neural Networks to Forecast Stock Market Prices Ramon Lawrence  

E-Print Network (OSTI)

Using Neural Networks to Forecast Stock Market Prices Ramon Lawrence Department of Computer Science on the application of neural networks in forecasting stock market prices. With their ability to discover patterns. Section 3 covers current analytical and computer methods used to forecast stock market prices

Lawrence, Ramon

102

Short term wind power forecasting using time series neural networks  

Science Conference Proceedings (OSTI)

Forecasting wind power energy is very important issue in a liberalized market and the prediction tools can make wind energy be competitive in these kinds of markets. This paper will study an application of time-series and neural network for predicting ... Keywords: neural networks, time series, wind power forecasting

Mohammadsaleh Zakerinia; Seyed Farid Ghaderi

2011-04-01T23:59:59.000Z

103

Application on lithology recognition with BP artificial neural network  

Science Conference Proceedings (OSTI)

An Artificial Neural Network (ANN) model is established to recognize the drilled formations' lithologies while drilling. The styles of output and input of ANN are designed. The nerve cells in input layer are weight of bit (WOB), speed of rotary (SOR) ... Keywords: artificial neural network, drilling, formation lithology, recognition

Jinhui Zhou; Jienian Yan; Li Pan

2009-11-01T23:59:59.000Z

104

Downscaling Precipitation and Temperature with Temporal Neural Networks  

Science Conference Proceedings (OSTI)

The issues of downscaling the outputs of a global climate model (GCM) to a scale that is appropriate to hydrological impact studies are investigated using a temporal neural network approach. The time-lagged feed-forward neural network (TLFN) is ...

Paulin Coulibaly; Yonas B. Dibike; François Anctil

2005-08-01T23:59:59.000Z

105

Modelling of Turkey's net energy consumption using artificial neural network  

Science Conference Proceedings (OSTI)

The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using artificial neural network (ANN) technique in order to determine the future level of the energy consumption in Turkey. Two different models ... Keywords: Turkey, artificial neural networks, energy forecasting, energy sources, estimation, gross generation, net energy consumption

Adnan Sozen; Erol Arcaklioglu; Mehmet Ozkaymak

2005-04-01T23:59:59.000Z

106

Predict strength of rubberized concrete using atrificial neural network  

Science Conference Proceedings (OSTI)

In this paper, behaviour of rubberized concrete was modelled using artificial neural network ANN and obtained results were compared to experimental data. Experimental test include recycling 5, 10, 15 and 20% percentage of concrete aggregate with different ... Keywords: artificial neural network, multi linear regression, root mean square, rubberized concrete

A. Abdollahzadeh; R. Masoudnia; S. Aghababaei

2011-02-01T23:59:59.000Z

107

Neural network learning of optimal Kalman prediction and control  

Science Conference Proceedings (OSTI)

Although there are many neural network (NN) algorithms for prediction and for control, and although methods for optimal estimation (including filtering and prediction) and for optimal control in linear systems were provided by Kalman in 1960 (with nonlinear ... Keywords: Kalman control, Kalman filter, Local cortical circuit, Recurrent neural network

Ralph Linsker

2008-11-01T23:59:59.000Z

108

Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network  

Science Conference Proceedings (OSTI)

Artificial neural networks (ANNs) and Bayesian belief networks (BBNs) utilizing select environmental variables were developed and evaluated, with the intent to model net ecosystem metabolism (a proxy for system trophic state) within a freshwater wetland. ... Keywords: Artificial neural networks, Bayesian belief networks, Knowledge extraction, Net ecosystem metabolism

William A. Young, II; David F. Millie; Gary R. Weckman; Jerone S. Anderson; David M. Klarer; Gary L. Fahnenstiel

2011-10-01T23:59:59.000Z

109

Neural networks letter: Delay-dependent stability analysis for continuous-time BAM neural networks with Markovian jumping parameters  

Science Conference Proceedings (OSTI)

This paper investigates the problem of stability analysis for bidirectional associative memory (BAM) neural networks with Markovian jumping parameters. Some new delay-dependent stochastic stability criteria are derived based on a novel Lyapunov-Krasovskii ... Keywords: BAM neural networks, Delay-dependence, Linear matrix inequality (LMI), Markovian jump

Hongyang Liu; Yan Ou; Jun Hu; Tingting Liu

2010-04-01T23:59:59.000Z

110

Delayed switching applied to memristor neural networks  

Science Conference Proceedings (OSTI)

Magnetic flux and electric charge are linked in a memristor. We reported recently that a memristor has a peculiar effect in which the switching takes place with a time delay because a memristor possesses a certain inertia. This effect was named the ''delayed switching effect.'' In this work, we elaborate on the importance of delayed switching in a brain-like computer using memristor neural networks. The effect is used to control the switching of a memristor synapse between two neurons that fire together (the Hebbian rule). A theoretical formula is found, and the design is verified by a simulation. We have also built an experimental setup consisting of electronic memristive synapses and electronic neurons.

Wang, Frank Z.; Yang Xiao; Lim Guan [Future Computing Group, School of Computing, University of Kent, Canterbury (United Kingdom); Helian Na [School of Computer Science, University of Hertfordshire, Hatfield (United Kingdom); Wu Sining [Xyratex, Havant (United Kingdom); Guo Yike [Department of Computing, Imperial College, London (United Kingdom); Rashid, Md Mamunur [CERN, Geneva (Switzerland)

2012-04-01T23:59:59.000Z

111

Mineral Wool Production Monitoring Using Neural Networks  

E-Print Network (OSTI)

Homogeneity of the primary layer in mineral wool production process is required for high quality products. State-of-the-art measurement techniques for the evaluation of primary layer homogeneity are very slow and can only be applied after the product is manufactured. We present here a method that enables on-line monitoring and control and is based on experimental modeling using neural networks. The experimental method is based on image acquisition and image processing of the mineral wool primary layer structure. As a estimator of the mineral wool primary layer structure and quality, the weight of the primary wool layer is used, measured by an on- line weighting device in four locations of the conveyor belt. The instrumentation of on- line weighting device was upgraded for the purpose of the present experiment and enabled high speed acquisition of all measurement channels. The structure of the mineral wool primary layer was measured by visualization of the modified entrance to the on- line balance using a CCD camera. All data channels were simultaneously sampled. Radial basis neural networks are used for prediction. The structure of the mineral wool primary layer is predicted on the basis of experimentally provided weights data. The learning set consists of weights- images pairs. The prediction of the mineral wool primary layer structure consists of providing only weights. A good agreement between statistical properties of measured and modeled structures of the primary wool layer like spatial homogeneity of the primary mineral wool layer thickness, is shown. The results of the study confirm that the time- delayed vector of weights bears enough information for the monitoring of the production process. The modeling of primary mineral wool structure is of lesser quality due to high dimensionality of the modeled variable.

Marko Ho?evar; Brane Širok; Bogdan Blagojevi?

2005-01-01T23:59:59.000Z

112

Prediction of flow pattern of gas-liquid flow through circular microchannel using probabilistic neural network  

Science Conference Proceedings (OSTI)

The present study attempts to develop a flow pattern indicator for gas-liquid flow in microchannel with the help of artificial neural network (ANN). Out of many neural networks present in literature, probabilistic neural network (PNN) has been chosen ... Keywords: Hydrodynamics, Microchannel, Microstructure, Multiphase flow, Probabilistic neural network, Transition boundary, Turbulence

Seim Timung; Tapas K. Mandal

2013-04-01T23:59:59.000Z

113

Modeling of effluent COD in UAF reactor treating cyanide containing wastewater using artificial neural network approaches  

Science Conference Proceedings (OSTI)

In this study the performance of the upflow anaerobic filter (UAF) reactor treating cyanide was simulated using three different neural network techniques (ANNs) - multi-layer perceptron (MLP) neural network, radial basis neural network (RBNN), and generalized ... Keywords: Anaerobic treatment, Artificial neural networks, Cyanide, Inhibition, Modelling, Waste water treatment

Turan Yilmaz; Galip Seckin; Ahmet Yuceer

2010-07-01T23:59:59.000Z

114

Prediction of subsidence due to underground mining by artificial neural networks  

Science Conference Proceedings (OSTI)

Alternatively to empirical prediction methods, methods based on influential functions and on mechanical model, artificial neural networks (ANNs) can be used for the surface subsidence prediction. In our case, the multi-layer feed-forward neural network ... Keywords: approximation of functions, artificial neural network, mining damage, multi-layer feed-forward neural network, subsidence prediction

Tomaž Ambroži?; Goran Turk

2003-06-01T23:59:59.000Z

115

Developing measurement selection strategy for neural network models  

Science Conference Proceedings (OSTI)

The paper deals with an application of the theory of optimum experimental design to the problem of selecting the data set for developing neural models. Another objective is to show that neural network trained with the samples obtained according to D-optimum ...

Przemys?aw Pretki; Marcin Witczak

2005-09-01T23:59:59.000Z

116

Artificial neural network in gaseous emissions prediction with bioreactor usage  

Science Conference Proceedings (OSTI)

The artificial neural network is used more and more often for prediction of processes related with the biowaste management. In this area, composting is one of the most important process of biowaste recycling. However, the gaseous emissions from the composted ... Keywords: composting, data acquisition, emissions, multilayer perceptron, neural modeling, prediction

Piotr Boniecki; Jacek Dach; Krzysztof Pilarski; Aleksander J?dru?; Krzysztof Nowakowski; Hanna Piekarska-Boniecka; Jacek Przyby?

2012-05-01T23:59:59.000Z

117

A Study of Experimental Evaluations of Neural Network Learning Algorithms  

E-Print Network (OSTI)

, published by MIT Press. From Neural Networks, all articles of volume 6 1993 and all 1 In this report, I will use the term evaluation to mean experimental evaluation. articles from numbers 1 to 5 of volume 7 1994 were used. From Neural Computation, all articles of volume 5 1993 and all articles from numbers 1 to 4

Prechelt, Lutz

118

Self-generation ART neural network for character recognition  

Science Conference Proceedings (OSTI)

In this paper, we present a novel self-generation, supervised character recognition algorithm based on adaptive resonance theory (ART) artificial neural network (ANN) and delta-bar-delta method. By combining two methods, the proposed algorithm can reduce ...

Taekyung Kim; Seongwon Lee; Joonki Paik

2006-05-01T23:59:59.000Z

119

The Application of Neural Network to the Development of Single ...  

Science Conference Proceedings (OSTI)

Neural Network. Like a human brain NN has a computational structure such that ... of the common cost function is the sum of squared error E(w) expressed by n.

120

Temperature Profiling with Neural Network Inversion of Microwave Radiometer Data  

Science Conference Proceedings (OSTI)

A neural network is used to obtain vertical profiles of temperature from microwave radiometer data. The overall rms error in the retrieved profiles of a test dataset was only about 8% worse than the overall error using an optimized statistical ...

James H. Churnside; Thomas A. Stermitz; Judith A. Schroeder

1994-02-01T23:59:59.000Z

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

Fuzzy ART neural network parallel computing on the GPU  

Science Conference Proceedings (OSTI)

Graphics Processing Units (GPUs) have evolved into powerful programmable processors, faster than Central Processing Units (CPUs) regarding the execution of parallel algorithms. In this paper, an implementation of a Fuzzy ART Neural Network on the GPU ...

Mario Martínez-Zarzuela; Francisco Javier Díaz Pernas; Josél Fernando Dííez Higuera; Míriam Antón Rodríguez

2007-06-01T23:59:59.000Z

122

Training of Neural Networks: Interactive Possibilities in a Distributed Framework  

Science Conference Proceedings (OSTI)

Training of Artificial Neural Networks in a Distributed Environment is considered and applied to a typical example in High Energy Physics interactive analysis. Promising results showing a reduction of the wait time from 5 hours to 5 minutes obtained ...

O. Ponce; J. Cuevas; A. Fuentes; J. Marco; R. Marco; C. Martínez-Rivero; R. Menéndez; D. Rodríguez

2002-09-01T23:59:59.000Z

123

Fuzzy diagnosis in AHU system using dynamic fuzzy neural network  

Science Conference Proceedings (OSTI)

In this paper, an efficient fault diagnosis method for air-handling unit using dynamic fuzzy neural networks (DFNNs) is presented. The proposed fault diagnosis method has the following salient features: (1) structure identification and parameters estimation ...

Du Juan; Er Meng Joo

2003-12-01T23:59:59.000Z

124

Probabilistic neural network classification for model ?-Glucan suspensions  

Science Conference Proceedings (OSTI)

The problems encountered in brewing commonly attributed to excess ?-glucan levels include low extract yield, increased lauter runoff times, formation of gelatinous precipitates during aging, and decreased filtration efficiency. Several rheological ... Keywords: ?-glucan, PNN, critical concentration, neural network, relative viscosity

Ratchadaporn Oonsivilai; Anant Oonsivilai

2007-09-01T23:59:59.000Z

125

Time delay learning by gradient descent in recurrent neural networks  

Science Conference Proceedings (OSTI)

Recurrent Neural Networks (RNNs) possess an implicit internal memory and are well adapted for time series forecasting. Unfortunately, the gradient descent algorithms which are commonly used for their training have two main weaknesses: the slowness and ...

Romuald Boné; Hubert Cardot

2005-09-01T23:59:59.000Z

126

Application of artificial neural networks to predicate shale content  

Science Conference Proceedings (OSTI)

This paper describes an Artificial Neural Network approach to the predication problem of shale content in the reservoir. An interval of seismic data representing the zone of interest is extracted from a three-dimensional data volume. Seismic data and ...

Kesheng Wang; Resko Barna; Yi Wang; Maxim Boldin; Ove R. Hjelmervik

2005-05-01T23:59:59.000Z

127

A Bayesian Neural Network for Severe-Hail Size Prediction  

Science Conference Proceedings (OSTI)

The National Severe Storms Laboratory has developed algorithms that compute a number of Doppler radar and environmental attributes known to be relevant for the detection/prediction of severe hail. Based on these attributes, two neural networks ...

Caren Marzban; Arthur Witt

2001-10-01T23:59:59.000Z

128

Neural Network Control of Robot Manipulators and Nonlinear Systems  

Science Conference Proceedings (OSTI)

From the Publisher:This graduate text provides an authoritative account of neural network (NN) controllers for robotics and nonlinear systems and gives the first textbook treatment of a general and streamlined design procedure for NN controllers. Stability ...

F. L. Lewis; A. Yesildirak; Suresh Jagannathan

1998-09-01T23:59:59.000Z

129

Neural Network Based Intelligent Sootblowing System  

SciTech Connect

Cost effective generation of electricity is vital to the economic growth and stability of this nation. To accomplish this goal a balanced portfolio of fuel sources must be maintained and established which not only addresses the cost of conversion of these energy sources to electricity, but also does so in an efficient and environmentally sound manner. Conversion of coal as an energy source to produce steam for a variety of systems has been a cornerstone of modern industry. However, the use of coal in combustion systems has traditionally produced unacceptable levels of gaseous and particulate emissions, albeit that recent combustion, removal and mitigation techniques have drastically reduced these levels. With the combustion of coal there is always the formation and deposition of ash and slag within the boilers. This adversely affects the rate at which heat is transferred to the working fluid, which in the case of electric generators is water/steam. The fouling of the boiler leads to poor efficiencies due to the fact that heat which could normally be transferred to the working fluid remains in the flue gas stream and exits to the environment without beneficial use. This loss in efficiency translates to higher consumption of fuel for equivalent levels of electric generation; hence more gaseous emissions are also produced. Another less obvious problem exists with fouling of various sections of the boiler creating intense peak temperatures within and around the combustion zone. Total nitrogen oxides (NOx) generation is primarily a function of both ''fuel'' and ''thermal'' NOx production. Fuel NOx which generally comprises 20%-40% of the total NOx generated is predominantly influenced by the levels of oxygen present, while thermal NOx which comprises the balance is a function of temperature. As the fouling of the boiler increases and the rate of heat transfer decreases, peak temperatures increase as does the thermal NOx production. Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

Mark Rhode

2005-04-01T23:59:59.000Z

130

Neural Network Based Intelligent Sootblowing System  

SciTech Connect

Cost effective generation of electricity is vital to the economic growth and stability of this nation. To accomplish this goal a balanced portfolio of fuel sources must be maintained and established which not only addresses the cost of conversion of these energy sources to electricity, but also does so in an efficient and environmentally sound manner. Conversion of coal as an energy source to produce steam for a variety of systems has been a cornerstone of modern industry. However, the use of coal in combustion systems has traditionally produced unacceptable levels of gaseous and particulate emissions, albeit that recent combustion, removal and mitigation techniques have drastically reduced these levels. With the combustion of coal there is always the formation and deposition of ash and slag within the boilers. This adversely affects the rate at which heat is transferred to the working fluid, which in the case of electric generators is water/steam. The fouling of the boiler leads to poor efficiencies due to the fact that heat which could normally be transferred to the working fluid remains in the flue gas stream and exits to the environment without beneficial use. This loss in efficiency translates to higher consumption of fuel for equivalent levels of electric generation; hence more gaseous emissions are also produced. Another less obvious problem exists with fouling of various sections of the boiler creating intense peak temperatures within and around the combustion zone. Total nitrogen oxides (NOx) generation is primarily a function of both ''fuel'' and ''thermal'' NOx production. Fuel NOx which generally comprises 20%-40% of the total NOx generated is predominantly influenced by the levels of oxygen present, while thermal NOx which comprises the balance is a function of temperature. As the fouling of the boiler increases and the rate of heat transfer decreases, peak temperatures increase as does the thermal NOx production. Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

Mark Rhode

2005-04-01T23:59:59.000Z

131

A dynamic K-winners-take-all neural network  

Science Conference Proceedings (OSTI)

In this paper, a dynamic K-winners-take-all (KWTA) neural network, which can quickly identify the K-winning neurons whose activations are larger than the remaining ones, is proposed and analyzed. For N competitors, the proposed KWTA network is composed ...

Jar-Ferr Yang; Chi-Ming Chen

1997-06-01T23:59:59.000Z

132

Back-propagating modes in elastic logging-while-drilling collars and their effect on PML stability  

Science Conference Proceedings (OSTI)

The Perfectly Matched Layer (PML) approach is widely used to implement the absorbing boundary conditions for coupled multi-physics wave propagation problems. However, it has been recognized that the solution in the PML absorbing layer can become unstable ... Keywords: Acoustic logging, Back-propagating mode, Borehole acoustics, Coupled problems, Perfectly matched layer, Wave propagation

Pawe? Jerzy Matuszyk, Carlos Torres-Verdín

2013-12-01T23:59:59.000Z

133

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

134

Real-time neural network earthquake profile predictor  

DOE Patents (OSTI)

A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as well as to changes in the elastic properties of the media throughout the propagation path. The neural network is trained using seismogram data from earthquakes. Presented with a previously unseen earthquake, the neural network produces a profile of the complete earthquake signal using data from the first seconds of the signal. This offers a significant advance in the real-time monitoring, warning, and subsequent hazard minimization of catastrophic ground motion. 17 figs.

Leach, R.R.; Dowla, F.U.

1996-02-06T23:59:59.000Z

135

Real-time neural network earthquake profile predictor  

DOE Patents (OSTI)

A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as well as to changes in the elastic properties of the media throughout the propagation path. The neural network is trained using seismogram data from earthquakes. Presented with a previously unseen earthquake, the neural network produces a profile of the complete earthquake signal using data from the first seconds of the signal. This offers a significant advance in the real-time monitoring, warning, and subsequent hazard minimization of catastrophic ground motion.

Leach, Richard R. (Castro Valley, CA); Dowla, Farid U. (Castro Valley, CA)

1996-01-01T23:59:59.000Z

136

Fault detection in reaction wheel of a satellite using observer-based dynamic neural networks  

Science Conference Proceedings (OSTI)

This paper presents a methodology for the actuator fault detection in the satellite's attitude control system (ACS) by using a dynamic neural network based observer. In this methodology, a neural network is used to model a nonlinear dynamical system. ...

Zhongqi Li; Liying Ma; Khashayar Khorasani

2005-05-01T23:59:59.000Z

137

A Neural Network Approach for a Fast and Accurate Computation of a Longwave Radiative Budget  

Science Conference Proceedings (OSTI)

The authors have investigated the possibility of elaborating a new generation of radiative transfer models for climate studies based on the neural network technique. The authors show that their neural network–based model, NeuroFlux, can be used ...

F. Chevallier; F. Chéruy; N. A. Scott; A. Chédin

1998-11-01T23:59:59.000Z

138

Genetic algorithm based k-means fast learning artificial neural network  

Science Conference Proceedings (OSTI)

The K-means Fast Learning Artificial Neural Network (KFLANN) is a small neural network bearing two types of parameters, the tolerance, ? and the vigilance, ? In previous papers, it was shown that the KFLANN was capable of fast ...

Yin Xiang; Alex Tay Leng Phuan

2004-12-01T23:59:59.000Z

139

Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks  

DOE Green Energy (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

140

Nonlinear system control using self-evolving neural fuzzy inference networks with reinforcement evolutionary learning  

Science Conference Proceedings (OSTI)

This study presents a reinforcement evolutionary learning algorithm (REL) for the self-evolving neural fuzzy inference networks (SENFIN). By applying functional link neural networks (FLNN) as the consequent part of the fuzzy rules, the proposed SENFIN ... Keywords: Cultural algorithm, Neural fuzzy inference network, Particle swarm optimization, Reinforcement learning

Cheng-Jian Lin; Cheng-Hung Chen

2011-12-01T23:59:59.000Z

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.


141

Applying a Weighting Matrix to the Hierarchical Neural Network Model for Handwritten Thai Character Recognition  

Science Conference Proceedings (OSTI)

This paper proposes a new neural network approach to the off-line handwritten Thai character recognition. This new neural network is a hierarchical neural network; it employs the concept of a weighting matrix in measuring the similarity between the incoming ...

Arit Thammano; Patcharawadee Poolsamran

2006-11-01T23:59:59.000Z

142

A genetic fuzzy radial basis function neural network for structural health monitoring of composite laminated beams  

Science Conference Proceedings (OSTI)

In this paper, a new neural network learning procedure, called genetic fuzzy hybrid learning algorithm (GFHLA) is proposed for training the radial basis function neural network (RBFNN). The method combines the genetic algorithm and fuzzy logic to optimize ... Keywords: Fuzzy logic, Genetic algorithm, RBF neural network, Structural health monitoring

Shi-jie Zheng; Zheng-qiang Li; Hong-tao Wang

2011-09-01T23:59:59.000Z

143

Neural network predictive control of UPFC for improving transient stability performance of power system  

Science Conference Proceedings (OSTI)

This paper presents a neural network predictive controller for the UPFC to improve the transient stability performance of the power system. A neural network model for the power system is trained using the backpropagation learning method employing the ... Keywords: Identification, Neural networks, Power system transient stability, Predictive control, Unified power flow controller (UPFC)

Sheela Tiwari; Ram Naresh; R. Jha

2011-12-01T23:59:59.000Z

144

Estimation of radiation damage at the structural materials of a hybrid reactor by probabilistic neural networks  

Science Conference Proceedings (OSTI)

This paper presents a new approach based on probabilistic neural networks (PNNs) for the radiation damage parameters at the structural material of a nuclear fusion-fission (hybrid) reactor. Artificial neural networks (ANNs) have recently been introduced ... Keywords: Atomic displacement, Helium generation, Hybrid reactor, Probabilistic neural networks (PNNs), Radiation damage

Elif Derya íbeyli; Mustafa íbeyli

2009-04-01T23:59:59.000Z

145

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

Science Conference Proceedings (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

146

Time Series Prediction of Mining Subsidence Based on Genetic Algorithm Neural Network  

Science Conference Proceedings (OSTI)

In order to find out the dynamics law of underground coal mining subsidence, BP neural network was used for time series prediction. First, genetic algorithm was used to optimize the initial network weight to overcome the inherent defects of BP neural ... Keywords: Mining subsidence, time series, BP neural network, genetic algorithm

Peixian Li; Zhixiang Tan; Lili Yan; Kazhong Deng

2011-07-01T23:59:59.000Z

147

Modeling the efficiency of top Arab banks: A DEA-neural network approach  

Science Conference Proceedings (OSTI)

This study investigates the efficiency of top Arab banks using two quantitative methodologies: data envelopment analysis and neural networks. The study uses a probabilistic neural network (PNN) and a traditional statistical classification method to model ... Keywords: Arab banks, Benchmarking, Data envelopment analysis, Discriminant analysis, Probabilistic neural networks, Relative efficiency

Mohamed M. Mostafa

2009-01-01T23:59:59.000Z

148

The stock index forecast based on dynamic recurrent neural network trained with GA  

E-Print Network (OSTI)

neural networks applied in forecasting stock price, at present, the most widely used neural network is BPThe stock index forecast based on dynamic recurrent neural network trained with GA Fang Yixian1In order to forecast the stock market more accurately, according to the dynamic property for the stock

149

Analysis of a variable speed vapor compression system using artificial neural networks  

Science Conference Proceedings (OSTI)

An artificial neural network (ANN) is a mathematical model that is inspired by the operation of biological neural networks. However, this is typically considered a computational model. An ANN can easily adapt to multiple situations and extract information ... Keywords: Artificial neural network, Energetic performance, Hidden neurons, Mapping configuration, Simulated annealing, Vapor compression system

J. M. Belman-Flores; S. E. Ledesma; M. G. Garcia; J. Ruiz; J. L. RodríGuez-MuñOz

2013-09-01T23:59:59.000Z

150

Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks  

E-Print Network (OSTI)

This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combined with fuzzy decision rule for interpretation of neural network outputs. Neural network learns the mapping between inputs and desired outputs through processing a set of example cases. Training of the neural network is based on the combined use of unsupervised and supervised learning methods. During training, a set of input events is transformed into a set of prototypes of typical input events. During application, real events are classified based on the interpretation of their matching to the prototypes through fuzzy decision rule. This study introduces several enhancements to the original version of the ART algorithm: suitable preprocessing of neural network inputs, improvement in the concept of supervised learning, fuzzyfication of neural network outputs, and utilization of on-line learning. A selected model of an actual power network is used to simulate extensive sets of scenarios covering a variety of power system operating conditions as well as fault and disturbance events. Simulation results show improved recognition capabilities compared to a previous version of ART neural network algorithm, Multilayer Perceptron (MLP) neural network algorithm, and impedance based distance relay. Simulation results also show exceptional robustness of the novel ART algorithm for all operating conditions and events studied, as well as superior classification capabilities compared to the other solutions. Consequently, it is demonstrated that the proposed ART solution may be used for accurate, high-speed distinction among faulted and unfaulted events, and estimation of fault type and fault section.

Vasilic, Slavko

2006-05-01T23:59:59.000Z

151

Computer-aided research into a natural neural network  

SciTech Connect

While artificial neural networks are being applied to solving problems in signal processing and other domains, much remains to be discovered about how even the simpler biological neural networks function. Studying simpler examples of natural systems promises to advance our understanding of principles of organization of neural tissue wherever it occurs. It also may prove useful in the development of new computer architectures. Accordingly, the authors have begun the study of the macular linear bioaccelerometers, or balance organs of mammals, using the rat as the model for the class. This effort includes creating a computer-based workbench that a scientist can use to generate geometric reconstructions of neural tissue from electron microscope serial sections, to create a functional model of information flow within the neural tissue, and ultimately to generate computer animations to visualize how the network functions. Their work to data is based upon the study of long series of sections in a transmission electron microscope. The sections are photographed and the photographs are assembled into montages. Selected nerves and receptor units synapsing with them (their receptive fields) are next traced onto transparencies from the montages. The tracings, which are cross-sectional contours, are digitized with a tablet and stored in data files on a personal computer. The files are transferred to a high performance graphics workstation, where software has been developed to reconstruct these sets of contours as polygonal objects, display them in wireframe or solid form, and create sequence files that can be used to produce a computer animation on videotape.

Ross, M.D.; Cutler, L.; Meyer, G.; Lam, T.; Or, W.

1988-09-01T23:59:59.000Z

152

Design of artificial neural networks for distribution feeder loss analysis  

Science Conference Proceedings (OSTI)

To enhance the efficiency for power loss analysis of voluminous distribution feeders, ANN-based simplified power loss models with the Levenberg-Marquardt (LM) algorithm have been developed for overhead feeders and underground feeders, respectively. The ... Keywords: Artificial neural network, Customer information system, Levenberg-Marquardt algorithm, Outage management system

Tsung-En Lee; Chin-Ying Ho; Chia-Hung Lin; Meei-Song Kang

2011-11-01T23:59:59.000Z

153

Fragile X syndrome: Neural network models of sequencing and memory  

Science Conference Proceedings (OSTI)

A comparative framework of memory processes in males with fragile X syndrome (FXS) and typically developing (TYP) mental age-match children is presented. Results indicate a divergence in sequencing skills, such that males with FXS recall sequences similarly ... Keywords: FXS, Fragile X syndrome, Intellectual disabilities, Literacy, Memory in atypical populations, Modeling, Neural networks, Phonology

Mina C. Johnson-Glenberg

2008-10-01T23:59:59.000Z

154

Successful neural network projects at the Idaho National Engineering Laboratory  

DOE Green Energy (OSTI)

This paper presents recent and current projects at the Idaho National Engineering Laboratory (INEL) that research and apply neural network technology. The projects are summarized in the paper and their direct application to space reactor power and propulsion systems activities is discussed. 9 refs., 10 figs., 3 tabs.

Cordes, G.A.

1991-01-01T23:59:59.000Z

155

Real-Time Forecasting of Snowfall Using a Neural Network  

Science Conference Proceedings (OSTI)

A set of 53 snowfall reports was collected in real time from the 2004/05 and 2005/06 cold seasons (November–March). Three snowfall-amount forecast methods were tested: neural network, surface-temperature-based 676-USDT table, and climatological ...

Paul J. Roebber; Melissa R. Butt; Sarah J. Reinke; Thomas J. Grafenauer

2007-06-01T23:59:59.000Z

156

Predict soil texture distributions using an artificial neural network model  

Science Conference Proceedings (OSTI)

High-resolution soil maps are important for planning agriculture crop production, forest management, hydrological analysis and environmental protection. However, high-resolution soil maps are generally only available for small areas because obtaining ... Keywords: Artificial neural network, Clay, DEM, High-resolution soil map, Sand, Soil texture

Zhengyong Zhao; Thien Lien Chow; Herb W. Rees; Qi Yang; Zisheng Xing; Fan-Rui Meng

2009-01-01T23:59:59.000Z

157

Optimisation of Concentrating Solar Thermal Power Plants with Neural Networks  

E-Print Network (OSTI)

Optimisation of Concentrating Solar Thermal Power Plants with Neural Networks Pascal Richter1 of solar power for energy supply is of in- creasing importance. While technical development mainly takes introduce our tool for the optimisation of parameterised solar thermal power plants, and report

Ábrahám, Erika

158

A fuzzy binary neural network for interpretable classifications  

Science Conference Proceedings (OSTI)

Classification is probably the most frequently encountered problem in machine learning (ML). The most successful ML techniques like multi-layer perceptrons or support vector machines constitute very complex systems and the underlying reasoning processes ... Keywords: Fuzzy reasoning, Machine learning, Neural networks, Supervised classification

Robert Meyer, Simon O'keefe

2013-12-01T23:59:59.000Z

159

3D porosity prediction from seismic inversion and neural networks  

Science Conference Proceedings (OSTI)

In this work, we address the problem of transforming seismic reflection data into an intrinsic rock property model. Specifically, we present an application of a methodology that allows interpreters to obtain effective porosity 3D maps from post-stack ... Keywords: Feed-forward neural network, Matlab, Reservoir characterization, Seismic inversion

Emilson Pereira Leite; Alexandre Campane Vidal

2011-08-01T23:59:59.000Z

160

Wind Power Plant Prediction by Using Neural Networks: Preprint  

DOE Green Energy (OSTI)

This paper introduces a method of short-term wind power prediction for a wind power plant by training neural networks based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data.

Liu, Z.; Gao, W.; Wan, Y. H.; Muljadi, E.

2012-08-01T23:59:59.000Z

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

Neural Networks for Real-Time Traffic Signal Control  

Science Conference Proceedings (OSTI)

Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper ... Keywords: Distributed control, hybrid model, neural control, online learning, traffic signal control

D. Srinivasan; Min Chee Choy; R. L. Cheu

2006-09-01T23:59:59.000Z

162

Air quality prediction in yinchuan by using neural networks  

Science Conference Proceedings (OSTI)

A field study was carried out in Yinchuan to gather and evaluate information about the real environment. O3 (Ozone), PM10 (particle 10 um in diameter and smaller) and SO2 (sulphur monoxide) constitute ... Keywords: air quality prediction, artificial neural networks, yinchuan

Fengjun Li

2010-06-01T23:59:59.000Z

163

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

E-Print Network (OSTI)

Special Issue Toyota Prius HEV neurocontrol and diagnostics$ Danil V. Prokhorov Toyota Technical Center, A division of Toyota Motor Engineering and Manufacturing North America (TEMA), Ann Arbor, MI 48105, United Abstract A neural network controller for improved fuel efficiency of the Toyota Prius hybrid electric

Prokhorov, Danil

164

Performance Prediction of Solar Collectors Using Artificial Neural Networks  

Science Conference Proceedings (OSTI)

A new approach based on artificial neural network (ANN) was developed in this study to determine the performance of solar collectors. The experiments were performed under the meteorological conditions of Beijing. Performance parameters obtained from ... Keywords: ANN, solar collector, performance prediction

Hui Xie; Li Liu; Fei Ma; Huifang Fan

2009-11-01T23:59:59.000Z

165

Self-consciousness for artificial entities using modular neural networks  

Science Conference Proceedings (OSTI)

One of the most puzzling cognitive features is self-consciousness. A considerable amount of research has been conducted on this question in different fields like Psychology, Neurobiology and Cognitive Science. Self-consciousness implies not only self ... Keywords: cognitive architecture, holonic system, modular neural networks, self-consciousness, self-representation

Milton Martinez Luaces; Celina Gayoso Rocha; Juan Pazos Sierra; Alfonso Rodriguez Patón

2008-05-01T23:59:59.000Z

166

An Evolutionary Artificial Neural Network for Medical Pattern Classification  

Science Conference Proceedings (OSTI)

In this paper, a novel evolutionary artificial neural network based on the integration between Fuzzy ARTMAP (FAM) and a Hybrid Genetic Algorithm (HGA) is proposed for tackling medical pattern classification tasks. To assess the effectiveness of the proposed ... Keywords: Fuzzy ARTMAP, Hybrid Genetic Algorithms, Medical Decision Support, Pattern Classification

Shing Chiang Tan; Chee Peng Lim; Kay Sin Tan; Jose C. Navarro

2009-12-01T23:59:59.000Z

167

Hindi paired word recognition using probabilistic neural network  

Science Conference Proceedings (OSTI)

Automatic speech recognition has been a subject of active research interest since last few decades. In the present paper, spoken Hindi (Indian national language) Paired Word Recognition (HPWR) has been examined with the help of intelligent ... Keywords: HPWR, Hindi, PNN, automatic speech recognition, broad acoustic classes, classification, hybrid computing, paired word recognition, pattern recognition, probabilistic neural networks, wavelet transforms

Dinesh Kumar Rajoriya; R. S. Anand; R. P. Maheshwari

2010-08-01T23:59:59.000Z

168

Artificial neural networks for automated year-round temperature prediction  

Science Conference Proceedings (OSTI)

Crops and livestock in most of the southeastern United States are susceptible to potential losses due to extreme cold and heat. However, given suitable warning, agricultural and horticultural producers can mitigate the damage of extreme temperature events. ... Keywords: Artificial intelligence, Frost protection, Fruit crops, Neural network, Temperature prediction, Vegetable crops

Brian A. Smith; Gerrit Hoogenboom; Ronald W. McClendon

2009-08-01T23:59:59.000Z

169

State estimation in bioprocesses: extended Kalman filter vs. neural network  

Science Conference Proceedings (OSTI)

In biotechnology the demand for process control strategies has increased during the last decades. As fermentation processes become more and more complex, increasing requirements are posed to the control tools. A high-level process control depends on ... Keywords: streptococcus thermophilus, bio-engineering, extended Kalman filter, neural networks, state estimation

J. Hörrmann; D. Barth; M. Kräling; H. Röck

2007-05-01T23:59:59.000Z

170

Prediction of surface roughness using artificial neural network in lathe  

Science Conference Proceedings (OSTI)

In this study, the effect of tool geometry on surface roughness has been investigated in universal lathe. Machining process has been carried out on AISI 1040 steel in dry cutting condition using various insert geometry at depth of cut off 0.5 mm. At ... Keywords: artificial neural network, surface roughness, tool geometry

?akir Ta?demir; Süleyman Ne?eli; Ismail Sarita?; Süleyman Yaldiz

2008-06-01T23:59:59.000Z

171

Artificial neural network modeling techniques applied to the hydrodesulfurization process  

Science Conference Proceedings (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

172

Forecasting ENSO Events: A Neural Network–Extended EOF Approach  

Science Conference Proceedings (OSTI)

The authors constructed neural network models to forecast the sea surface temperature anomalies (SSTA) for three regions: Niño 4, Niño 3.5, and Niño 3, representing the western-central, the central, and the eastern-central parts of the equatorial ...

Fredolin T. Tangang; Benyang Tang; Adam H. Monahan; William W. Hsieh

1998-01-01T23:59:59.000Z

173

Robust Heteroscedastic Probabilistic Neural Network for multiple source partial discharge pattern recognition - Significance of outliers on classification capability  

Science Conference Proceedings (OSTI)

Among various insulation diagnostic techniques utilized by researchers and personnel handling power equipment, partial discharge (PD) recognition and analysis has emerged as a vital methodology since it is inherently a non-intrusive testing strategy. ... Keywords: Heteroscedastic Probabilistic Neural Network (HRPNN), Neural network (NN), Partial discharge (PD), Probabilistic Neural Network (PNN), Robust Heteroscedastic Neural Network (RHRPNN)

S. Venkatesh; S. Gopal

2011-09-01T23:59:59.000Z

174

Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks  

Science Conference Proceedings (OSTI)

In this paper, next-day hourly forecasts are calculated for the energy price in the electricity production market of Spain. The methodology used to achieve these forecasts is based on artificial neural networks, which have been used successfully in recent ... Keywords: ART network, Backpropagation network, Box-Jenkins, Electricity market, Neural networks, Time series forecasting

Raúl Pino; José Parreno; Alberto Gomez; Paolo Priore

2008-02-01T23:59:59.000Z

175

Feedforward neural network and adaptive network-based fuzzy inference system in study of power lines  

Science Conference Proceedings (OSTI)

Over the past several decades, concerns have been raised over the possibility that the exposure to extremely low frequency electromagnetic fields from power lines may have harmful effects on human and living organisms. This paper presents novel approach ... Keywords: Adaptive network-based fuzzy inference systems, Electromagnetic fields, Feedforward neural network, Power lines

Jasna Radulovi?; Vesna Rankovi?

2010-01-01T23:59:59.000Z

176

A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms  

E-Print Network (OSTI)

dedicated 1 In this report, I will use the term evaluation to mean experimental evaluation. 1 #12;to neural network research, namely 1. Neural Networks NN, published by Perga- mon Press; all articles of Volume 6 1993and all articles from numbers 1 to 5 of Volume 7 1994. 2. Neural Computation NC, published

Prechelt, Lutz

177

A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms  

E-Print Network (OSTI)

dedicated 1 In this report, I will use the term evaluation to mean experimental evaluation. 1 #12; to neural network research, namely 1. Neural Networks (NN), published by Perga­ mon Press; all articles of Volume 6 (1993) and all articles from numbers 1 to 5 of Volume 7 (1994). 2. Neural Computation (NC), published

Prechelt, Lutz

178

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

179

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

180

Battery modeling for electric vehicle applications using neural networks  

SciTech Connect

Neural networking is a new approach to modeling batteries for electric vehicle applications. This modeling technique is much less complex then a first principles model but can consider more parameters then classic empirical modeling. Test data indicates that individual cell size and geometry and operating conditions affect a battery performance (energy density, power density and life). Given sufficient battery data, system parameters and operating conditions a neural network model could be used to interpolate and perhaps even extrapolate battery performance under wide variety of operating conditions. As a result the method could be a valuable design tool for electric vehicle battery design and application. This paper describes the on going modeling method at Texas A and M University and presents preliminary results of a tubular lead acid battery model. The ultimate goal of this modeling effort is to develop the values necessary to be able to predict performance for batteries as wide ranging as sodium sulfur to zinc bromine.

Swan, D.H.; Arikara, M.P.; Patton, A.D.

1993-12-31T23:59:59.000Z

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.


181

Rapid Speaker Adaptation for Neural Network Speech Recognizers  

E-Print Network (OSTI)

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : x 1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.1 Thesis Outline : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 2 Speech Recognition with Neural Networks : : : : : : : : : : : : : : : : : : 4 2.1 The Speech Recognition Problem : : : : : : : : : : : : : : : : : : : : : : : : 4 2.2 Hybrid Systems : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 2.2.1 Architecture : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8 2.2.2 Evaluation and Training : : : : : : : : : : : : : : : : : : : : : : : : : 8 3 Review of Adaptation Literature : : : : : : : : : : : : : : : : : : : : : : : : 13 3.1 Speaker Adaptation/Normalization : : : : : : : : : : : : : : : : : : : : : : : 13 3.1.1 Speaker Categorization Approaches : : : : : : : : : : : : : : : : : : : 16 3.1.2 Data/Feature Transformation Approaches : : : : : : : : : ...

Daniel Clark Burnett; Mark Fanty; Hynek Hermansky; Jordan Cohen

1997-01-01T23:59:59.000Z

182

Workshop on environmental and energy applications of neural networks  

SciTech Connect

This report consists of the abstracts for the papers given at the conference. Applications of neural networks in the environmental, energy and biomedical fields are discussed. Some of the topics covered are: predicting atmospheric pollutant concentrations due to fossil-fired electric power generation; hazardous waste characterization; nondestructive TRU (transuranic) waste assay; risk analysis; load forecasting for electric utilities; design of a wind power storage and generation system; nuclear fuel management; etc.

Hashem, S.

1995-03-01T23:59:59.000Z

183

Neural network uncertainty assessment using Bayesian statistics with application to remote sensing  

E-Print Network (OSTI)

Neural network uncertainty assessment using Bayesian statistics with application to remote sensing for many inversion problems in remote sensing; however, uncertainty estimates are rarely provided Meteorology and Atmospheric Dynamics: General or miscellaneous; KEYWORDS: remote sensing, uncertainty, neural

Aires, Filipe

184

Neural network Jacobian analysis for high-resolution profiling of the atmosphere  

E-Print Network (OSTI)

Neural networks have been widely used to provide retrievals of geophysical parameters from spectral radiance measurements made remotely by air-, ground-, and space-based sensors. The advantages of retrievals based on neural ...

Blackwell, William J.

185

Structural Impairment Detection Using Arrays of Competitive Artificial Neural Networks  

E-Print Network (OSTI)

Aging railroad bridge infrastructure is subject to increasingly higher demands such as heavier loads, increased speed, and increased frequency of traffic. The challenges facing railroad bridge infrastructure provide an opportunity to develop improved systems of monitoring railroad bridges. This dissertation outlines the development and implementation of a Structural Impairment Detection System (SIDS) that incorporates finite element modeling and instrumentation of a testbed structure, neural algorithm development, and the integration of data acquisition and impairment detection tools. Ultimately, data streams from the Salmon Bay Bridge are autonomously recorded and interrogated by competitive arrays of artificial neural networks for patterns indicative of specific structural impairments. Heel trunnion bascule bridges experience significant stress ranges in critical truss members. Finite element modeling of the Salmon Bay Bridge testbed provided an estimate of nominal structural behavior and indicated types and locations of possible impairments. Analytical modeling was initially performed in SAP2000 and then refined with ABAQUS. Modeling results from the Salmon Bay Bridge were used to determine measureable quantities sensitive to modeled impairments. An instrumentation scheme was designed and installed on the testbed to record these diagnostically significant data streams. Analytical results revealed that main chord members and bracing members of the counterweight truss are sensitive to modeled structural impairments. Finite element models and experimental observations indicated maximum stress ranges of approximately 22 ksi on main chord members of the counterweight truss. A competitive neural algorithm was developed to examine analytical and experimental data streams. Analytical data streams served as training vectors for training arrays of competitive neural networks. A quasi static array of neural networks was developed to provide an indication of the operating condition at specific intervals of the bridge's operation. Competitive neural algorithms correctly classified 94% of simulated data streams. Finally, a stand-alone application was integrated with the Salmon Bay Bridge data acquisition system to autonomously analyze recorded data streams and produce bridge condition reports. Based on neural algorithms trained on modeled impairments, the Salmon Bay Bridge operates in a manner most resembling one of two operating conditions: 1) unimpaired, or 2) impaired embedded member at the southeast corner of the counterweight.

Story, Brett

2012-05-01T23:59:59.000Z

186

Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks  

Science Conference Proceedings (OSTI)

Neural networks have been widely used for short-term, and to a lesser degree medium and long-term, demand forecasting. In the majority of cases for the latter two applications, multivariate modeling was adopted, where the demand time series is related ... Keywords: Abductive networks, Energy demand, Medium-term load forecasting, Neural networks, Time series forecasting, Univariate time series analysis

R. E. Abdel-Aal

2008-05-01T23:59:59.000Z

187

Load Forecasting on Special Days & Holidays in Power Distribution Substation Using Neural & Fuzzy Networks  

Science Conference Proceedings (OSTI)

The demand for neural and fuzzy network techniques to predict the increasing load and its application has changed to an ordinary action. However the facts of the real world caused special and exceptional conditions to be created in this network. Like ... Keywords: Power system, Load forecasting, neural & fuzzy network, Short-term prediction of load.

Saeid Nahi

2006-11-01T23:59:59.000Z

188

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

Science Conference Proceedings (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

189

Temperature and combustion quality control in power station boilers using artificial neural networks  

Science Conference Proceedings (OSTI)

The classification is an important domain in boiler flame image processing and is a preliminary step toward detection, recognition and understanding of combustion condition. In this paper, Back Propagation Algorithm (BPA) is introduced for boiler flame ... Keywords: Back Propagation Algorithm, Fisher's linear discriminant analysis, combustion quality, features, flame colour, image processing, temperature identification

K. Sujatha; N. Pappa

2010-09-01T23:59:59.000Z

190

CONDUCTING IN-SITU COMBUSTION TUBE EXPERIMENTS USING ARTIFICIAL NEURAL NETWORKS.  

E-Print Network (OSTI)

??Artificial neural networks (ANNs), also known as expert systems, have become an increasingly important part of the petroleum industry for performance analysis of reservoirs. ANNs… (more)

Bansal, Yogesh

2009-01-01T23:59:59.000Z

191

Topological and Dynamical Complexity of Random Neural Networks  

E-Print Network (OSTI)

Random neural networks are dynamical descriptions of randomly interconnected neural units. These show a phase transition to chaos as a disorder parameter is increased. The microscopic mechanisms underlying this phase transition are unknown, and similarly to spin-glasses, shall be fundamentally related to the behavior of the system. In this Letter we investigate the explosion of complexity arising near that phase transition. We show that the mean number of equilibria undergoes a sharp transition from one equilibrium to a very large number scaling exponentially with the dimension on the system. Near criticality, we compute the exponential rate of divergence, called topological complexity. Strikingly, we show that it behaves exactly as the maximal Lyapunov exponent, a classical measure of dynamical complexity. This relationship unravels a microscopic mechanism leading to chaos which we further demonstrate on a simpler class of disordered systems, suggesting a deep and underexplored link between topological and dynamical complexity.

Gilles Wainrib; Jonathan Touboul

2012-10-18T23:59:59.000Z

192

Based on Two Swarm Optimized Algorithm of Neural Network to Prediction the Switch's Traffic of Coal  

Science Conference Proceedings (OSTI)

Coal accurately predict multi-channel network traffic monitoring network for transmission to enhance and improve the QoS is very important, the characteristics of coalmine monitoring network, the first neural network model was constructed, followed by ... Keywords: Coal, network traffic, ant colony algorithm, Particle swarm optimization

Xiao-qiang Shao

2011-07-01T23:59:59.000Z

193

Using CODEQ to Train Feed-forward Neural Networks  

E-Print Network (OSTI)

CODEQ is a new, population-based meta-heuristic algorithm that is a hybrid of concepts from chaotic search, opposition-based learning, differential evolution and quantum mechanics. CODEQ has successfully been used to solve different types of problems (e.g. constrained, integer-programming, engineering) with excellent results. In this paper, CODEQ is used to train feed-forward neural networks. The proposed method is compared with particle swarm optimization and differential evolution algorithms on three data sets with encouraging results.

Omran, Mahamed G H

2010-01-01T23:59:59.000Z

194

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

195

Training Recurrent Neural Network Using Multistream Extended Kalman Filter on Multicore Processor and Cuda Enabled Graphic Processor Unit  

Science Conference Proceedings (OSTI)

Recurrent neural networks are popular tools used for modeling time series. Common gradient-based algorithms are frequently used for training recurrent neural networks. On the other side approaches based on the Kalman filtration are considered to be the ...

Michal ?er?anský

2009-09-01T23:59:59.000Z

196

Application of Neural Networks to the Simulation of the Heat Island over Athens, Greece, Using Synoptic Types as a Predictor  

Science Conference Proceedings (OSTI)

The effect of the synoptic-scale atmospheric circulation on the urban heat island phenomenon over Athens, Greece, was investigated and quantified for a period of 2 yr, employing a neural network approach. A neural network model was appropriately ...

Giouli Mihalakakou; Helena A. Flocas; Manthaios Santamouris; Costas G. Helmis

2002-05-01T23:59:59.000Z

197

Original paper: The prediction of seedy grape drying rate using a neural network method  

Science Conference Proceedings (OSTI)

This paper presents an application which uses Feedforward Neural Networks (FNNs) to model the nonlinear behaviour of the drying of seedy grapes. First, a novel type of dryer for experimentally and mathematically evaluating the thin-layer drying kinetics ... Keywords: Drying, Modelling, Neural networks, Seedy grape

Gül?ah Çakmak; Cengiz Y?ld?z

2011-01-01T23:59:59.000Z

198

Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification  

Science Conference Proceedings (OSTI)

An electroencephalogram (EEG) analysis system for single-trial classification of motor imagery (MI) data is proposed in this study. Unsupervised fuzzy Hopfield neural network (FHNN) clustering, together with active segment selection and multiresolution ... Keywords: Brain-computer interface (BCI), Electroencephalogram (EEG), Fractal dimension (FD), Fuzzy Hopfield neural network (FHNN), Motor imagery (MI), Wavelet transform

Wei-Yen Hsu

2012-01-01T23:59:59.000Z

199

Non-linear variable selection for artificial neural networks using partial mutual information  

Science Conference Proceedings (OSTI)

Artificial neural networks (ANNs) have been widely used to model environmental processes. The ability of ANN models to accurately represent the complex, non-linear behaviour of relatively poorly understood processes makes them highly suited to this task. ... Keywords: Artificial neural networks, Environmental modelling, Information theory, Input variable selection, Partial mutual information

Robert J. May; Holger R. Maier; Graeme C. Dandy; T.M.K. Gayani Fernando

2008-10-01T23:59:59.000Z

200

Simulation of T-S Fuzzy Neural Network to UASB Reactor Shocked by Toxic Loading  

Science Conference Proceedings (OSTI)

The neural network was conducted based on the Takagi-Sugeno fuzzy systems. Predictions of the biogas production rate, volatile fatty acid and CH4 for the UASB reactor were made using fuzzy neural network based on database collected from the anaerobic ...

Gang Cao; Mingyu Li; Cehui Mo

2007-12-01T23:59:59.000Z

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.


201

An artificial neural network approach for day-ahead electricity prices forecasting  

Science Conference Proceedings (OSTI)

This paper is about the use of artificial neural networks on day-ahead electricity prices forecasting. In nowadays competitive electricity markets, good forecasting tools hedging against daily price volatility are becoming increasingly important. The ... Keywords: artificial neural networks, electricity markets, prices forecasting

João Catalão; Sílvio Mariano; Victor Mendes; Luís Ferreira

2005-06-01T23:59:59.000Z

202

Integer-Encoded Massively Parallel Processing of Fast-Learning Fuzzy ARTMAP Neural Networks  

E-Print Network (OSTI)

A. Bahra , Ronald F. DeMarab , Michael N. Georgiopoulosb a HQ STRICOM, AMSTI-ET, 2350 Research categories are self-organized in neural networks1 . Since this time, a number of specific neural network-fold which resulted in a combined training and testing time of under 4 minutes. The organization

DeMara, Ronald F.

203

A hybrid neural network and ARIMA model for water quality time series prediction  

Science Conference Proceedings (OSTI)

Accurate predictions of time series data have motivated the researchers to develop innovative models for water resources management. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be ... Keywords: ARIMA, Backpropagation, Hybrid model, Neural networks, Time series

Durdu Ömer Faruk

2010-06-01T23:59:59.000Z

204

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

Paris-Sud XI, Université de

205

Wind Prediction Based on Improved BP Artificial Neural Network in Wind Farm  

Science Conference Proceedings (OSTI)

Wind power prediction is important to the operation of power system with comparatively large mount of wind power. It can relieve or avoid the disadvantageous impact of wind farm on power systems. Because the traditional neural network may fall into local ... Keywords: wind farm, wind power generation, wind speed prediction, BP neural networks

Keyuan Huang; Lang Dai; Shoudao Huang

2010-06-01T23:59:59.000Z

206

Fast time delay neural networks for word detection in video conference  

Science Conference Proceedings (OSTI)

This paper presents a new approach to speed up the operation of time delay neural networks for fast detecting a word in a video conference. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast ... Keywords: cross correlation, fast time delay neural networks, frequency domain, word detection in video conference

Hazem M. El-Bakry; Nikos Mastorakis

2009-06-01T23:59:59.000Z

207

Application and performance analysis of neural networks for decision support in conceptual design  

Science Conference Proceedings (OSTI)

This paper analyzes the use of feedforward multilayer perceptron neural networks in the support of the decision process during the conceptual design phase of engineering systems. A user friendly software tool is proposed in order to increase the quality ... Keywords: Conceptual design, Decision support, Neural networks

Ivo M. L. Ferreira; Paulo J. S. Gil

2012-07-01T23:59:59.000Z

208

Neural network control of air-to-fuel ratio in a bi-fuel engine  

Science Conference Proceedings (OSTI)

In this paper, a neural network-based control system is proposed for fine control of the intake air/fuel ratio in a bi-fuel engine. This control system is an add-on module for an existing vehicle manufacturer's electronic control units (ECUs). Typically ... Keywords: Artificial neural networks, bi-fuel engines, compressed natural gas (CNG), fuel injection control

G. Gnanam; S. R. Habibi; R. T. Burton; M. T. Sulatisky

2006-09-01T23:59:59.000Z

209

Combining Neural Network and Genetic Algorithm for Prediction of Lung Sounds  

Science Conference Proceedings (OSTI)

Recognition of lung sounds is an important goal in pulmonary medicine. In this work, we present a study for neural networks--genetic algorithm approach intended to aid in lung sound classification. Lung sound was captured from the chest wall of The subjects ... Keywords: MLP, auscultation, genetic algorithm, lung sounds, neural network, respiratory diseases

?nan Güler; Hüseyin Polat; Uçman Ergün

2005-06-01T23:59:59.000Z

210

Automatic design of artificial neural networks and associative memories for pattern classification and pattern restoration  

Science Conference Proceedings (OSTI)

In this note we present our most recent advances in the automatic design of artificial neural networks (ANNs) and associative memories (AMs) for pattern classification and pattern recall. Particle Swarm Optimization (PSO), Differential Evolution (DE), ... Keywords: artificial neural networks, associative memories, evolutionary programming

Humberto Sossa; Beatriz A. Garro; Juan Villegas; Carlos Avilés; Gustavo Olague

2012-06-01T23:59:59.000Z

211

Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching  

Science Conference Proceedings (OSTI)

A novel fully automated system is introduced to facilitate lesion detection in dynamic contrast-enhanced, magnetic resonance mammography (DCE-MRM). The system extracts breast regions from pre-contrast images using a cellular neural network, generates ... Keywords: 3D template matching, Cellular neural network, Lesion detection, MR mammography, Segmentation

Gökhan Erta?; H.Özcan Gülçür; Onur Osman; Osman N. Uçan; Mehtap Tunac?; Memduh Dursun

2008-01-01T23:59:59.000Z

212

A neural network control strategy for improved energy capture on a variable-speed wind turbine  

Science Conference Proceedings (OSTI)

Pitch control has so far been the dominating method for power control in modern variable speed wind turbines. This paper proposes an improved control technique for pitching the blades of a variable speed wind turbine, using Artificial Neural Networks ... Keywords: artificial neural networks, control trajectories, pitch control, variable-speed wind turbines

António F. Silva; Fernando A. Castro; José N. Fidalgo

2005-06-01T23:59:59.000Z

213

Exploration of artificial neural network to predict morphology of TiO2 nanotube  

Science Conference Proceedings (OSTI)

Artificial neural network (ANN) was developed to predict the morphology of TiO"2 nanotube prepared by anodization. The collected experimental data was simplified in an innovative approach and used as training and validation data, and the morphology of ... Keywords: Anodization, Artificial neural network, Morphology, Prediction, TiO2 nanotube

Hongyi Zhang; Jianling Zhao; Yuying Jia; Xuewen Xu; Cencun Tang; Yangxian Li

2012-03-01T23:59:59.000Z

214

Modeling plasma surface modification of textile fabrics using artificial neural networks  

Science Conference Proceedings (OSTI)

In this paper, a neural network approach is used to understand the effects of fabric features and plasma processing parameters on fabric surface wetting properties. In this approach, fourteen features characterizing woven structures and two plasma parameters ... Keywords: Artificial neural networks, Atmospheric air-plasma, Fuzzy logic based selection criterion, Modeling, Surface wetting properties, Woven fabrics

Radhia Abd Jelil, Xianyi Zeng, Ludovic Koehl, Anne Perwuelz

2013-09-01T23:59:59.000Z

215

Artificial neural networks for predicting dorsal pressures on the foot surface while walking  

Science Conference Proceedings (OSTI)

In this work, artificial neural networks (ANNs) are proposed to predict the dorsal pressure over the foot surface exerted by the shoe upper while walking. A model that is based on the multilayer perceptron (MLP) is used since it can provide a single ... Keywords: Artificial neural networks, Dorsal pressures, Multilayer perceptron, Shoe upper

M. J. Rupérez; J. D. Martín-Guerrero; C. Monserrat; M. Alcañiz

2012-04-01T23:59:59.000Z

216

Prediction of the electron flux environment in geosynchronous orbit using a neural network technique  

Science Conference Proceedings (OSTI)

In this study, a neural network technique is adopted to predict the electron flux in a geosynchronous orbit using several items of solar wind data obtained by ACE spacecraft and magnetic variations observed on the ground as input parameters. Parameter ... Keywords: Internal charging, Neural network, Spacecraft

K. Kitamura; Y. Nakamura; M. Tokumitsu; Y. Ishida; S. Watari

2011-12-01T23:59:59.000Z

217

A neural network-based estimation of electric fields along high voltage insulators  

Science Conference Proceedings (OSTI)

This paper presents a two-dimensional (2D) electric fields estimation program to calculate the field distribution along the leakage distance of an insulator under polluted conditions using artificial neural network (ANN). A fog type suspension insulator ... Keywords: Electric fields, Insulator, Neural networks, Pollution

Zafer Aydogmus

2009-05-01T23:59:59.000Z

218

Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules  

Science Conference Proceedings (OSTI)

This study proposes a fuzzy neural network (FNN) that can process both fuzzy inputs and outputs. The continuous genetic algorithm (CGA) is employed to enhance its performance. Both the simulation and real-world problem results show that the proposed ... Keywords: Continuous genetic algorithms, Fuzzy neural networks

R. J. Kuo; S. M. Hong; Y. Lin; Y. C. Huang

2008-08-01T23:59:59.000Z

219

Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas  

Science Conference Proceedings (OSTI)

The transport of sediment and nutrients from land application areas is an environmental concern. New methods are needed for estimating soil and nutrient concentrations of runoff from cropland areas on which manure is applied. Artificial Neural Networks ... Keywords: Artificial Neural Network (ANN), Land application, Manure runoff, Nitrogen, Nutrient losses, Phosphorus, Soil erosion, Water quality

Minyoung Kim; John E. Gilley

2008-12-01T23:59:59.000Z

220

Seismic damage identification in buildings using neural networks and modal data  

Science Conference Proceedings (OSTI)

A seismic damage identification method intended for buildings with steel moment-frame structure is presented in this paper. The method has a statistical approach and is based on artificial neural networks and modal variables. It consists of two main ... Keywords: Mass sensitivity, Modal data, Neural networks, Seismic damage identification, Steel frames, Transmission of errors

María P. González; José L. Zapico

2008-02-01T23:59:59.000Z

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.


221

2012 Special Issue: Nonlinear dynamics and chaos in fractional-order neural networks  

Science Conference Proceedings (OSTI)

Several topics related to the dynamics of fractional-order neural networks of Hopfield type are investigated, such as stability and multi-stability (coexistence of several different stable states), bifurcations and chaos. The stability domain of a steady ... Keywords: Chaos, Fractance, Fractional order, Hopf bifurcation, Hub, Multistability, Neural networks, Ring, Stability, Strange attractor

Eva Kaslik; Seenith Sivasundaram

2012-08-01T23:59:59.000Z

222

Performance prediction of a ground-coupled heat pump system using artificial neural networks  

Science Conference Proceedings (OSTI)

This paper describes the applicability of artificial neural networks (ANNs) to predict performance of a horizontal ground-coupled heat pump (GCHP) system. Performance forecasting is the precondition for the optimal control and energy saving operation ... Keywords: Artificial neural network, Coefficient of performance, Ground-coupled heat pump, Horizontal heat exchanger, Learning algorithm

Hikmet Esen; Mustafa Inalli; Abdulkadir Sengur; Mehmet Esen

2008-11-01T23:59:59.000Z

223

Analysis on delay-dependent stability for neural networks with time-varying delays  

Science Conference Proceedings (OSTI)

This paper considers the problem of delay-dependent stability criteria for neural networks with time-varying delays. First, by constructing a newly augmented Lyapunov-Krasovskii functional, a less conservative stability criterion is established in terms ... Keywords: Lyapunov method, Neural networks, Stability, Time-varying delays

O. M. Kwon; Ju H. Park; S. M. Lee; E. J. Cha

2013-03-01T23:59:59.000Z

224

Modeling the competitive market efficiency of Egyptian companies: A probabilistic neural network analysis  

Science Conference Proceedings (OSTI)

Understanding efficiency levels is crucial for understanding the competitive structure of a market and/or segments of a market. This study uses two artificial neural networks (NN) and a traditional statistical classification method to classify the relative ... Keywords: Data envelopment analysis, Discriminant analysis, Market efficiency, Probabilistic neural networks

Mohamed M. Mostafa

2009-07-01T23:59:59.000Z

225

Delay Dependent Exponential Stability for Fuzzy Recurrent Neural Networks with Interval Time-Varying Delay  

Science Conference Proceedings (OSTI)

In this paper, the problem of delay-dependent exponential stability for fuzzy recurrent neural networks with interval time-varying delay is investigated. The delay interval has been decomposed into multiple non equidistant subintervals, on these interval ... Keywords: Delay decomposition, Fuzzy recurrent neural networks, Interval time-varying delay, Maximum admissible upper bound (MAUB), Maximum exponential convergent rate (MECR)

R. Chandran; P. Balasubramaniam

2013-04-01T23:59:59.000Z

226

Wavelet-based image watermarking with visibility range estimation based on HVS and neural networks  

Science Conference Proceedings (OSTI)

This work proposes a wavelet-based image watermarking (WIW) technique, based on the human visible system (HVS) model and neural networks, for image copyright protection. A characteristic of the HVS, which is called the just noticeable difference (JND) ... Keywords: Human visual system, Image watermarking, Just noticeable difference, Neural networks, Wavelet transformation

Hung-Hsu Tsai; Chi-Chih Liu

2011-04-01T23:59:59.000Z

227

Modelling of time related drying changes on matte coated paper with artificial neural networks  

Science Conference Proceedings (OSTI)

In this study, the determinability of time related colour changes in prints made using ink that dries on matte coated paper with the offset printing technique and infrared method, has been investigated with artifical neural networks after analysis of ... Keywords: Artificial neural networks, Colour changes, Offset printing

Türkün ?ahïnba?kan; Erdo?an Köse

2010-04-01T23:59:59.000Z

228

An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring  

Science Conference Proceedings (OSTI)

Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for ... Keywords: Accurate, Artificial neural network, Bearing, Prediction, Remaining useful life

Zhigang Tian

2012-04-01T23:59:59.000Z

229

Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors  

Science Conference Proceedings (OSTI)

In this study we present a neural network model for predicting the methane fraction in landfill gas originating from field-scale landfill bioreactors. Landfill bioreactors were constructed at the Odayeri Sanitary Landfill, Istanbul, Turkey, and operated ... Keywords: Anaerobic digestion, Landfill gas, Leachate, Methane fraction, Modeling, Neural network

Bestamin Ozkaya; Ahmet Demir; M. Sinan Bilgili

2007-06-01T23:59:59.000Z

230

Self-tuning PID control of hydro-turbine governor based on genetic neural networks  

Science Conference Proceedings (OSTI)

A genetic neural networks (GNN) control strategy for hydroturbine governor is proposed in this paper. Considering the complex dynamic characteristic and uncertainty of the hydro-turbine governor model and taking the static and dynamic performance of ... Keywords: PID control, genetic algorithm, hydro-turbine governor, neural network

Aiwen Guo; Jiandong Yang

2007-09-01T23:59:59.000Z

231

Model for estimating Venezuelan population with working age using artificial neural networks  

Science Conference Proceedings (OSTI)

This work presents the development of an Artificial Neural Networks model for estimating the female and male population with working age in Venezuela. For the creation of the model it is used the previous year values related to the employed, unemployed ... Keywords: artificial neural networks, labor force, regression analysis

Samaria Muñoz-Bravo; Anna Pérez-Méndez; Francklin Rivas-Echeverría

2009-03-01T23:59:59.000Z

232

A forecasting system for car fuel consumption using a radial basis function neural network  

Science Conference Proceedings (OSTI)

A predictive system for car fuel consumption using a radial basis function (RBF) neural network is proposed in this paper. The proposed work consists of three parts: information acquisition, fuel consumption forecasting algorithm and performance evaluation. ... Keywords: Artificial neural network, Car fuel consumption, Radial basis function algorithm

Jian-Da Wu; Jun-Ching Liu

2012-02-01T23:59:59.000Z

233

Challenges for large-scale implementations of spiking neural networks on FPGAs  

Science Conference Proceedings (OSTI)

The last 50 years has witnessed considerable research in the area of neural networks resulting in a range of architectures, learning algorithms and demonstrative applications. A more recent research trend has focused on the biological plausibility of ... Keywords: Field programmable gate arrays (FPGAs), Hardware implementation, Spiking neural network (SNN)

L. P. Maguire; T. M. McGinnity; B. Glackin; A. Ghani; A. Belatreche; J. Harkin

2007-12-01T23:59:59.000Z

234

An application of neural network solution in the apparel industry for cutting time forecasting  

Science Conference Proceedings (OSTI)

Artificial Neural Network is proposed to handle to estimating of "marker making" cutting time. Estimating the optimum cutting time greatly affects the production efficiency and daily production capacity of the cutting department. Using ANN system in ... Keywords: Levenberg-Marquardt algorithm, apparel industry, artificial neural network, calculating of fabric lays quantities, fabric cutting time, length of marker making

Yelda Ozel; Mahmut Kayar

2008-09-01T23:59:59.000Z

235

Combining artificial neural networks and heuristic rules in a hybrid intelligent load forecast system  

Science Conference Proceedings (OSTI)

In this work, an Artificial Neural Network (ANN) is combined to Heuristic Rules producing a powerful hybrid intelligent system for short and mid-term electric load forecasting. The Heuristic Rules are used to adjust the ANN output to improve the system ... Keywords: artificial neural networks, electric load forecast, heuristic rules, hybrid system

Ronaldo R. B. de Aquino; Aida A. Ferreira; Manoel A. Carvalho, Jr.; Milde M. S. Lira; Geane B. Silva; Otoni Nóbrega Neto

2006-09-01T23:59:59.000Z

236

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

Science Conference Proceedings (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

237

Modelling of a vertical ground coupled heat pump system by using artificial neural networks  

Science Conference Proceedings (OSTI)

This paper describes the applicability of artificial neural networks (ANNs) to estimate of performance of a vertical ground coupled heat pump (VGCHP) system used for cooling and heating purposes experimentally. The system involved three heat exchangers ... Keywords: Cooling, Ground, Heat exchanger, Heat pump, Heating, Neural network

Hikmet Esen; Mustafa Inalli

2009-09-01T23:59:59.000Z

238

Estimation of heat transfer in oscillating annular flow using artifical neural networks  

Science Conference Proceedings (OSTI)

In this study, the prediction of heat transfer from a surface having constant heat flux subjected to oscillating annular flow is investigated using artificial neural networks (ANNs). An experimental study is carried out to estimate the heat transfer ... Keywords: Annular duct, Artificial neural network, Heat transfer, Oscillating flow

Unal Akdag; M. Aydin Komur; A. Feridun Ozguc

2009-09-01T23:59:59.000Z

239

Forecasting Electricity Demand on Short, Medium and Long Time Scales Using Neural Networks  

Science Conference Proceedings (OSTI)

This paper examines the application of artificial neural networks (ANNs) to the modelling and forecasting of electricity demand experienced by an electricity supplier. The data used in the application examples relates to the national electricity demand ... Keywords: Box–Jenkins model, artificial neural networks, electrical load, electricity demand, load forecasting

J. V. Ringwood; D. Bofelli; F. T. Murray

2001-05-01T23:59:59.000Z

240

Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model  

Science Conference Proceedings (OSTI)

The application of artificial neural network (ANN) to rainfall-runoff simulations has provided promising results in recent years. However, it is difficult to obtain satisfying results by using raw data for the direct prediction of the time series of ... Keywords: BP neural network, Data partitioning, Flood period, Rainfall-runoff simulations, Xinanjiang model

Qin Ju; Zhongbo Yu; Zhenchun Hao; Gengxin Ou; Jian Zhao; Dedong Liu

2009-08-01T23:59:59.000Z

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.


241

Constructing neural network sediment estimation models using a data-driven algorithm  

Science Conference Proceedings (OSTI)

Artificial neural network (ANN) models are designed for suspended sediment estimation using statistical pre-processing of the data. Statistical properties such as cross-, auto- and partial auto-correlation of the data series are used for identifying ... Keywords: A data-driven algorithm, Neural networks, Sediment estimation

Özgür Kisi

2008-10-01T23:59:59.000Z

242

Application of artificial neural network for predicting weld quality in laser transmission welding of thermoplastics  

Science Conference Proceedings (OSTI)

The present work establishes a correlation between the laser transmission welding parameters and output variables though a nonlinear model, developed by applying artificial neural network (ANN). The process parameters of the model include laser power, ... Keywords: Artificial neural networks, Laser transmission welding, Regression analysis, Sensitivity analysis, Thermoplastics

Bappa Acherjee; Subrata Mondal; Bipan Tudu; Dipten Misra

2011-03-01T23:59:59.000Z

243

Improved robust stability criteria for delayed cellular neural networks via the LMI approach  

Science Conference Proceedings (OSTI)

Uniqueness and robust exponential stability are analyzed for a class of uncertain cellular neural networks with time-varying delays. By dividing the variation interval of the time delay into two subintervals with equal length, a novel Lyapunov-Krasovskii ... Keywords: free-weighting matrix method, global robust exponential stability, linear matrix inequality (LMI), uncertain cellular neural networks

Cheng-De Zheng; Huaguang Zhang; Zhanshan Wang

2010-01-01T23:59:59.000Z

244

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, similar in shape and size to a Chevrolet S-10, which was converted to an electric vehicle

Rudnick, Hugh

245

Oil Price Forecasting with an EMD-Based Multiscale Neural Network Learning Paradigm  

Science Conference Proceedings (OSTI)

In this study, a multiscale neural network learning paradigm based on empirical mode decomposition (EMD) is proposed for crude oil price prediction. In this learning paradigm, the original price series are first decomposed into various independent intrinsic ... Keywords: Crude oil price forecasting, artificial neural networks, empirical mode decomposition, multiscale learning paradigm

Lean Yu; Kin Keung Lai; Shouyang Wang; Kaijian He

2007-05-01T23:59:59.000Z

246

MPPT of Solar Energy Generating System with Fuzzy Control and Artificial Neural Network  

Science Conference Proceedings (OSTI)

In order to achieve maximum power of solar cell, we focus on the maximum power point tracking (MPPT) algorithm forming based on fuzzy control. The fuzzy control rules are adopted using artificial neural network with measured data. Compared the fuzzy ... Keywords: Maximum power point tracking, Fuzzy control, artificial neural network, simulation

Keya Huang; Wenshi Li; Xiaoyang Huang

2011-09-01T23:59:59.000Z

247

Technology independent circuit sizing for standard cell based design using neural networks  

Science Conference Proceedings (OSTI)

This paper presents a neural network (NN) approach for modeling the time characteristics of fundamental gates of digital integrated circuits that include inverter, NAND, NOR, and XOR gates. The modeling approach presented here is technology independent, ... Keywords: Computer aided design, Digital integrated circuits, Neural networks

Nihan Kahraman; Tulay Yildirim

2009-07-01T23:59:59.000Z

248

Medical image diagnosis of liver cancer by feedback GMDH-type neural network using knowledge base  

Science Conference Proceedings (OSTI)

A revised group method of data handling (GMDH)-type neural network algorithm using knowledge base for medical image diagnosis, is proposed and applied to medical image diagnosis of liver cancer. In this algorithm, the knowledge base for medical image ... Keywords: Artificial intelligence, GMDH, Medical image diagnosis, Neural networks

Tadashi Kondo; Junji Ueno; Shoichiro Takao

2013-02-01T23:59:59.000Z

249

Power quality disturbances classification based on S-transform and probabilistic neural network  

Science Conference Proceedings (OSTI)

Classifying power quality (PQ) disturbances is one of the most important issues for power quality control. A novel high-performance classification system based on the S-transform and a probabilistic neural network (PNN) is proposed. The original power ... Keywords: Power quality, Power quality disturbances, Probabilistic neural network, S-transform

Nantian Huang; Dianguo Xu; Xiaosheng Liu; Lin Lin

2012-12-01T23:59:59.000Z

250

Quantum Gauged Neural Network: U(1) Gauge Theory  

E-Print Network (OSTI)

A quantum model of neural network is introduced and its phase structure is examined. The model is an extension of the classical Z(2) gauged neural network of learning and recalling to a quantum model by replacing the Z(2) variables, $S_i = \\pm1$ of neurons and $J_{ij} =\\pm1$ of synaptic connections, to the U(1) phase variables, $S_i = \\exp(i\\phi_i)$ and $J_{ij} = \\exp(i\\theta_{ij}) $. These U(1) variables describe the phase parts of the wave functions (local order parameters) of neurons and synaptic connections. The model takes the form similar to the U(1) Higgs lattice gauge theory, the continuum limit of which is the well known Ginzburg-Landau theory of superconductivity. Its current may describe the flow of electric voltage along axons and chemical materials transfered via synaptic connections. The phase structure of the model at finite temperatures is examined by the mean-field theory, and Coulomb, Higgs and confinement phases are obtained. By comparing with the result of the Z(2) model, the quantum effects is shown to weaken the ability of learning and recalling.

Yukari Fujita; Tetsuo Matsui

2002-06-30T23:59:59.000Z

251

How well can radiologists using neural network software diagnose pulmonary embolism  

E-Print Network (OSTI)

This study evaluated and optimized the performance of an automated artificial neural network image interpreter in the diagnosis of pulmonary embolism on ventilation– perfusion lung scans. The computer interpretations were compared with the interpretations of three experienced observers. MATERIALS AND METHODS. Digital data were obtained from 100 patients with normal findings on chest radiographs who were undergoing both radionuclide ventilation–perfusion scanning and pulmonary angiography. Interpretations of differently trained neural networks were compared with those of three experienced nuclear medicine practitioners unaware of the clinical diagnosis. RESULTS. Machines running neural networks performed similarly to experienced scan interpreters in the detection of pulmonary embolism. Both the human observers and the networks performed best in cases with large emboli. Neural network performance was best in the right lung, when the networks were trained using only cases with large emboli and when networks

James A. Scott; Edwin L. Palmer; Alan J. Fischman

2000-01-01T23:59:59.000Z

252

Evolutionary programming versus artificial immune system in evolving neural network for grid-connected photovoltaic system output prediction  

Science Conference Proceedings (OSTI)

This paper presents the evolutionary neural networks for the prediction of energy output from a grid-connected photovoltaic (GCPV) system. Two evolutionary neural network (ENN) models have been proposed using evolutionary programming and artificial immune ... Keywords: artificial immune system (AIS) and prediction, artificial neural network (ANN), correlation coefficient (R), evolutionary programming (EP), grid-connected photovoltaic system (GCPV), multi-layer feedforward neural network (MLFNN), photovoltaic (PV)

Shahril Irwan Sulaiman; Titik Khawa Abdul Rahman; Ismail Musirin; Sulaiman Shaari

2011-06-01T23:59:59.000Z

253

Neural Fault Diagnosis and Fuzzy Fault Control for a Complex Dynamic System  

E-Print Network (OSTI)

Fault diagnosis has become an issue of primary importance in modern process automation as it provides the prerequisites for the task of fault detection. The ability to detect the faults is essential to improve reliability and security of a complex control system. Parameter estimation methods, state observation schemes, statistical likelihood ratio tests, rule-based expert system reasoning, pattern recognition techniques, and artificial neural network approaches are the most common methodologies developed actively during recent years. In this paper, we describe a completed feasibility study demonstrating the merit of employing pattern recognition and an artificial neural network for fault diagnosis through back propagation learning algorithm and making the use of fuzzy approximate reasoning for fault control via parameter changes in a dynamic system. As a test case, a complex magnetic levitation vehicle (MLV) system is studied. Analytical fault symptoms are obtained by system dynamics m...

Ching-yu Tyan; Paul P. Wang; Dennis R. Bahler

1995-01-01T23:59:59.000Z

254

APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION  

Science Conference Proceedings (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

255

Two-photon exchange effect studied with neural networks  

SciTech Connect

An approach to the extraction of the two-photon exchange (TPE) correction from elastic ep scattering data is presented. The cross-section, polarization transfer (PT), and charge asymmetry data are considered. It is assumed that the TPE correction to the PT data is negligible. The form factors and TPE correcting term are given by one multidimensional function approximated by the feedforward neural network (NN). To find a model-independent approximation, the Bayesian framework for the NNs is adapted. A large number of different parametrizations is considered. The most optimal model is indicated by the Bayesian algorithm. The obtained fit of the TPE correction behaves linearly in {epsilon} but it has a nontrivial Q{sup 2} dependence. A strong dependence of the TPE fit on the choice of parametrization is observed.

Graczyk, Krzysztof M. [Institute of Theoretical Physics, University of Wroclaw, pl. M. Borna 9, PL-50-204 Wroclaw (Poland)

2011-09-15T23:59:59.000Z

256

Neural networks as tools for predicting materials properties  

Science Conference Proceedings (OSTI)

Materials science is of fundamental significance to science and technology because our industrial base and society depend upon our ability to develop advanced materials. Materials and materials processing cuts across almost every sector of industry. The key in all of these areas is the ability to rapidly screen possible designs which will have significant impact. However up to now materials design and processing have been to a large extent empirical sciences. In addition we are still unable to design new alloys and polymers to meet application specific requirements. Being able to do so quickly and at minimum cost would provide an incredible advantage. Obviously, the ability to predict physical, chemical, or mechanical properties of compounds prior to their synthesis is of great technological value in optimizing their design, processing, or recycling. In addition, in order to realize the ultimate goal of materials by computational design, the reverse problem, prediction of chemical structure based on desired properties, has to be resolved. Research at ORNL has lead to the development of a novel computational paradigm (coupling computational neural networks with graph theory, genetic algorithms, wavelet theory, fuzzy logic, molecular dynamics, and quantum chemistry) capable of performing accurate computational synthesis (both predictions of properties or the design of compounds that have specified performance criteria). The computational paradigm represents a hybrid of a number of emerging technologies and has proven to work very well for test compounds ranging from small organic molecules to polymeric materials. Fundamental to the method is the neural network-based formulation of the correlations between structure and properties. The advantages of this method is in its ease of use, speed, accuracy, and that it can be used to predict both properties from structure, and also structure from properties.

Sumpter, B.G.; Noid, D.W.

1995-12-31T23:59:59.000Z

257

Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage  

Science Conference Proceedings (OSTI)

The motivation of this paper is to investigate the use of a Neural Network (NN) architecture, the Psi Sigma Neural Network (PSN), when applied to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate using the European Central Bank ... Keywords: Forecast combinations, Kalman Filter, LASSO, Leverage, Psi Sigma network, Recurrent Network

Georgios Sermpinis; Christian Dunis; Jason Laws; Charalampos Stasinakis

2012-12-01T23:59:59.000Z

258

Image fusion algorithms for color and gray level images based on LCLS method and novel artificial neural network  

Science Conference Proceedings (OSTI)

In this paper, two neural image fusion algorithms for color and gray level images are proposed. These algorithms are based on a linearly constrained least square (LCLS) method and a novel projection recurrent artificial neural network. The theoretical ... Keywords: Image fusion, LCLS method, Neural networks, Real-time applications, Stability and convergence analysis

Alaeddin Malek; Maryam Yashtini

2010-01-01T23:59:59.000Z

259

Using neural networks and cellular automata for modelling intra-urban land-use dynamics  

Science Conference Proceedings (OSTI)

Empirical models designed to simulate and predict urban land-use change in real situations are generally based on the utilization of statistical techniques to compute the land-use change probabilities. In contrast to these methods, artificial neural ... Keywords: Cellular automata, Fuzzy similarity measures, Land-use dynamics, Neural networks, Town planning, Urban modelling

C. M. Almeida; J. M. Gleriani; E. F. Castejon; B. S. Soares-Filho

2008-09-01T23:59:59.000Z

260

Blind separation with unknown number of sources based on auto-trimmed neural network  

Science Conference Proceedings (OSTI)

This paper focuses on blind source separation with an unknown number of sources, which is the case generally assumed in most practical applications. Several over-determined neural algorithms (more sensors m than sources n) have been proposed to solve ... Keywords: Auto-trimmed neural network, Blind source separation (BSS), Unknown number of sources

Tsung-Ying Sun; Chan-Cheng Liu; Sheng-Ta Hsieh; Shang-Jeng Tsai

2008-06-01T23:59:59.000Z

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.


261

Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data  

Science Conference Proceedings (OSTI)

In the light of recent advances in spectral imaging technology, highly flexible modeling methods must be developed to estimate various soil and crop parameters for precision farming from airborne hyperspectral imagery. The potential of artificial neural ... Keywords: Artificial neural networks, CASI, Corn, Crop yield, Hyperspectral remote sensing, Precision agriculture

Y. Uno; S. O. Prasher; R. Lacroix; P. K. Goel; Y. Karimi; A. Viau; R. M. Patel

2005-05-01T23:59:59.000Z

262

Non-stationary Signal Forecasting by Neural Network with Modified Neurons  

Science Conference Proceedings (OSTI)

This paper presents the non-stationary power signal forecasting by using a neural network with modified neurons for PJM data set provided by Independent Electricity System Operator (IESO). In this data set, the load information is the sum of power load ... Keywords: load, forecasting, neural model, modified neurons

Chih-Chien Huang; Yi-Ching Lin; Yu-Ju Chen; Shuming T. Wang; Rey-Chue Hwang

2010-03-01T23:59:59.000Z

263

Estimation of Nitrogen Removal Effect in Groundwater Using Artificial Neural Network  

Science Conference Proceedings (OSTI)

Groundwater contamination by nitrate is a globally growing problem. Biological denitrification is a simple and cost effective method. However, this process is non-linear, complex and multivariable. This paper presents the application of artificial neural ... Keywords: artificial neural networks (ANN), groundwater, nitrogen removal

Jinlong Zuo

2008-09-01T23:59:59.000Z

264

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.

265

Artificial Neural Networks and Long-Range Precipitation Prediction in California  

Science Conference Proceedings (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

266

Plenary lecture 6: neural networks: a bridge towards self-observation  

Science Conference Proceedings (OSTI)

Regardless of their increasing number and diversity, the capacities of Neural Network (NN) models still remain far behind the ones biological systems can exhibit when faced to changing environments or other complex processes. As an attempt to better ...

Jean-Jacques Mariage

2009-06-01T23:59:59.000Z

267

Peak ground velocity evaluation by artificial neural network for west america region  

Science Conference Proceedings (OSTI)

With the Peak Ground Velocity 283 records in three dimensions, the velocity attenuation relationship with distance was discussed by neural network in this paper. The earthquake magnitude, epicenter distance, site intensity and site condition were considered ...

Ben-yu Liu; Liao-yuan Ye; Mei-ling Xiao; Sheng Miao

2006-10-01T23:59:59.000Z

268

Simulation of Significant Wave Height by Neural Networks and Its Application to Extreme Wave Analysis  

Science Conference Proceedings (OSTI)

The derivation of the long-term statistical distribution of significant wave heights (Hss) is discussed in this paper. The distribution parameters are estimated using artificial neural networks (ANNs) trained with the help of a simulated ...

A. Aminzadeh-Gohari; H. Bahai; H. Bazargan

2009-04-01T23:59:59.000Z

269

Parameter Identification of Vibration Loads of Hydro Generator Using Neural Networks  

Science Conference Proceedings (OSTI)

Abstract Abstract Abstract Vibrating dynamic characteristics have been a major unknown in the modeling and mechanical analysis of large hydro generators. An identification algorithm for vibrating dynamic characterization by using artificial neural network ...

Lijuan Cao; Shouju Li; Zichang Shangguan

2008-06-01T23:59:59.000Z

270

Retrieval of Atmospheric Temperature Profiles from AMSU-A Measurement Using a Neural Network Approach  

Science Conference Proceedings (OSTI)

Backpropagation neural networks are applied to retrieve atmospheric temperature profiles and tropopause variables from the NOAA-15 Advanced Microwave Sounding Unit-A (AMSU-A) measurement based on two different data sources. The first case uses ...

Lei Shi

2001-03-01T23:59:59.000Z

271

An Artificial Neural Network Approach to Multispectral Rainfall Estimation over Africa  

Science Conference Proceedings (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

272

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

Science Conference Proceedings (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

273

Performance comparison of meta-heuristic algorithms for training artificial neural networks in modelling laser cutting  

Science Conference Proceedings (OSTI)

The application of artificial neural networks ANNs for modelling laser cutting is broad and ever increasing. The practical application of ANNs is mostly dependent on the success of the training process which is a complex task. Considering the disadvantages ...

Miloš Madi?; Danijel Markovi?; Miroslav Radovanovi?

2012-02-01T23:59:59.000Z

274

Neural Network Mapping of Magnet Based Position Sensing System for Autonomous Robotic Vehicle  

Science Conference Proceedings (OSTI)

In this paper a neural network mapping of magnet based position sensing system for an autonomous robotic vehicle. The position sensing system using magnetic markers embedded under the surface of roadway pavement. An important role of magnetic position ...

Dae---Yeong Im; Young-Jae Ryoo; Jang-Hyun Park; Hyong-Yeol Yang; Ju-Sang Lee

2007-04-01T23:59:59.000Z

275

Rainfall Estimation from Polarimetric S-Band Radar Measurements: Validation of a Neural Network Approach  

Science Conference Proceedings (OSTI)

A procedure for the estimation of rainfall rate, capitalizing on a radar-based raindrop size distribution (RSD) parameter retrieval and neural network (NN) inversion techniques, is validated using an extensive and quality-controlled archive. The ...

Gianfranco Vulpiani; Scott Giangrande; Frank S. Marzano

2009-10-01T23:59:59.000Z

276

A general rate K/N convolutional decoder based on neural networks with stopping criterion  

Science Conference Proceedings (OSTI)

A novel algorithm for decoding a general rate K/Nconvolutional code based on recurrent neural network (RNN) is described and analysed. The algorithm is introduced by outlining the mathematical models of the encoder and decoder. A number of strategies ...

Johnny W. H. Kao; Stevan M. Berber; Abbas Bigdeli

2009-01-01T23:59:59.000Z

277

Decadal Climate Simulations Using Accurate and Fast Neural Network Emulation of Full, Longwave and Shortwave, Radiation  

Science Conference Proceedings (OSTI)

An approach to calculating model physics using neural network emulations, previously proposed and developed by the authors, has been implemented in this study for both longwave and shortwave radiation parameterizations, or to the full model ...

Vladimir M. Krasnopolsky; Michael S. Fox-Rabinovitz; Alexei A. Belochitski

2008-10-01T23:59:59.000Z

278

Enhanced recognition rate of spoken Hindi paired word using probabilistic neural network approach  

Science Conference Proceedings (OSTI)

Probabilistic neural network (PNN) shows efficient capability in recognising low level signal patterns. PNN is a sequential arrangement of radial basis layer and a competitive transfer function layer, which picks up the highest probabilities ...

Dinesh Kumar Rajoriya; R. S. Anand; R. P. Maheshwari

2011-08-01T23:59:59.000Z

279

Combining artificial neural networks and statistics for stock-market forecasting  

Science Conference Proceedings (OSTI)

We have developed a stock-market forecasting system based on artificial neural networks. The system has been trained with the Standard & Poor 500 composite indexes of past twenty years. Meanwhile, the system produces the forecasts and adjusts ...

Shaun-Inn Wu; Ruey-Pyng Lu

1993-03-01T23:59:59.000Z

280

Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks  

E-Print Network (OSTI)

Investing in the bulk carrier market constitutes a rather risky investment due to the volatility of the bulk carrier freight rates. In this study it is attempted to uncover the benefits of using Artificial Neural Networks ...

Voudris, Athanasios V

2006-01-01T23:59:59.000Z

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.


281

Crude Oil Price Forecasting with an Improved Model Based on Wavelet Transform and RBF Neural Network  

Science Conference Proceedings (OSTI)

The fluctuation of oil price decides the security of energy and economics. So the crude oil price forecasting performs importantly. In the paper, we apply the improved model based on Wavelet Transform and Radial Basis Function (RBF) neural network to ...

Wu Qunli; Hao Ge; Cheng Xiaodong

2009-05-01T23:59:59.000Z

282

Currency options volatility forecasting with shift-invariant wavelet transform and neural networks  

Science Conference Proceedings (OSTI)

This paper describes four currency options volatility forecasting models. These models are based on shift-invariant wavelet transform and neural networks techniques. The à trous algorithm is used to realize the shift-invariant wavelet transform. ...

Fan-Yong Liu; Fan-Xin Liu

2006-10-01T23:59:59.000Z

283

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

284

Neural networks based multiplex forecasting system of the end-point of copper blow period  

Science Conference Proceedings (OSTI)

The neural network and the experiential evaluation method are introduced into the industrial converting process forecast, and a multiplex forecast system is proposed at the end-point of copper blow period in a matte converting process. The fuzzy clustering ...

Lihua Xue; Hongzhong Huang; Yaohua Hu; Zhangming Shi

2005-05-01T23:59:59.000Z

285

Neural network application for radionuclide modelling and prediction of radioactivity levels  

Science Conference Proceedings (OSTI)

Existing applications of artificial neural networks in physics research and development have been analyzed as a basis for proposing new opportunities using that AI technology for data analysis in physics. A taxonomy was developed, based on an extensive ...

Myron Corbett Lynch, Jr. / Larry Medsker

2004-01-01T23:59:59.000Z

286

Extraction of Piecewise-Linear Analog Circuit Models from Trained Neural Networks Using Hidden Neuron Clustering  

Science Conference Proceedings (OSTI)

This paper presents a new technique for automatically creating analog circuit models. The method extracts - from trained neural networks - piecewise linear models expressing the linear dependencies between circuit performances and design parameters. ...

Simona Doboli; Gaurav Gothoskar; Alex Doboli

2003-03-01T23:59:59.000Z

287

Observed Relationships between Arctic Longwave Cloud Forcing and Cloud Parameters Using a Neural Network  

Science Conference Proceedings (OSTI)

A neural network technique is used to quantify relationships involved in cloud–radiation feedbacks based on observations from the Surface Heat Budget of the Arctic (SHEBA) project. Sensitivities of longwave cloud forcing (CFL) to cloud parameters ...

Yonghua Chen; Filipe Aires; Jennifer A. Francis; James R. Miller

2006-08-01T23:59:59.000Z

288

Nuclear mass systematics by complementing the Finite Range Droplet Model with neural networks  

E-Print Network (OSTI)

A neural-network model is developed to reproduce the differences between experimental nuclear mass-excess values and the theoretical values given by the Finite Range Droplet Model. The results point to the existence of subtle regularities of nuclear structure not yet contained in the best microscopic/phenomenological models of atomic masses. Combining the FRDM and the neural-network model, we create a hybrid model with improved predictive performance on nuclear-mass systematics and related quantities.

S. Athanassopoulos; E. Mavrommatis; K. A. Gernoth; J. W. Clark

2005-11-30T23:59:59.000Z

289

Application of neural network for air-fuel ratio identification in spark ignition engine  

Science Conference Proceedings (OSTI)

In the present work, Recurrent Neural Network (RNN) is used for Air-Fuel Ratio (AFR) identification in Spark Ignition (SI) engine. AFR identification is difficult due to nonlinear and dynamic behaviour of SI engines. Delays present in the engine ... Keywords: AFR sensors, RNNs, air-fuel ratio control, air-fuel ratio sensors, engine modelling, recurrent neural networks, simulation, spark ignition engines, virtual sensors

Samir Saraswati; Satish Chand

2008-10-01T23:59:59.000Z

290

An artificial neural network system for diagnosing gas turbine engine fuel faults  

DOE Green Energy (OSTI)

The US Army Ordnance Center & School and Pacific Northwest Laboratories are developing a turbine engine diagnostic system for the M1A1 Abrams tank. This system employs Artificial Neural Network (AN) technology to perform diagnosis and prognosis of the tank`s AGT-1500 gas turbine engine. This paper describes the design and prototype development of the ANN component of the diagnostic system, which we refer to as ``TEDANN`` for Turbine Engine Diagnostic Artificial Neural Networks.

Illi, O.J. Jr. [Army Ordnance Center and School, Aberdeen Proving Ground, MD (United States). Knowledge Engineering Group (KEG); Greitzer, F.L.; Kangas, L.J. [Pacific Northwest Lab., Richland, WA (United States); Reeve, T. [Expert Solutions, Stratford, CT (United States)

1994-04-01T23:59:59.000Z

291

ANALYSIS AND OPTIMIZATION OF GAS- CENTRIFUGAL SEPARATION OF URANIUM ISOTOPES BY NEURAL NETWORKS  

E-Print Network (OSTI)

Abstract- Neural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1 % was found.

unknown authors

2002-01-01T23:59:59.000Z

292

Tropical cyclone identification and tracking system using integrated neural oscillatory elastic graph matching and hybrid RBF network track mining techniques  

Science Conference Proceedings (OSTI)

We present an automatic and integrated neural network-based tropical cyclone (TC) identification and track mining system. The proposed system consists of two main modules: 1) TC pattern identification system using neural oscillatory elastic graph matching ...

R. S.T. Lee; J. N.K. Liu

2000-05-01T23:59:59.000Z

293

Evidence for single top quark production using Bayesian neural networks  

SciTech Connect

We present results of a search for single top quark production in p{bar p} collisions using a dataset of approximately 1 fb{sup -1} collected with the D0 detector. This analysis considers the muon+jets and electron+jets final states and makes use of Bayesian neural networks to separate the expected signals from backgrounds. The observed excess is associated with a p-value of 0.081%, assuming the background-only hypothesis, which corresponds to an excess over background of 3.2 standard deviations for a Gaussian density. The p-value computed using the SM signal cross section of 2.9 pb is 1.6%, corresponding to an expected significance of 2.2 standard deviations. Assuming the observed excess is due to single top production, we measure a single top quark production cross section of {sigma}(p{bar p} {yields} tb + X, tqb + X) = 4.4 {+-} 1.5 pb.

Kau, Daekwang; /Florida State U.

2007-08-01T23:59:59.000Z

294

Application of Functional Link Neural Network to HVAC Thermal Dynamic System Identification  

E-Print Network (OSTI)

Abstract — Recent efforts to incorporate aspects of artificial intelligence into the design and operation of automatic control systems have focused attention on techniques such as fuzzy logic, artificial neural networks, and expert systems. The use of computers for direct digital control highlights the recent trend toward more effective and efficient heating, ventilating, and airconditioning (HVAC) control methodologies. Researchers in the HVAC field have stressed the importance of self learning in building control systems and have encouraged further studies in the integration of optimal control and other advanced techniques into the formulation of such systems. Artificial neural networks can also be used to emulate the plant dynamics, in order to estimate future plant outputs and obtain plant input/output sensitivity information for on-line neural control adaptation. This paper describes a functional link neural network approach to performing the HVAC thermal dynamic system identification. Methodologies to reduce inputs of the functional link network to reduce the complexity and speed up the training speed will be presented. Analysis and comparison between the functional link network approach and the conventional network approach for the HVAC thermal modeling will also be presented. Index Terms—Functional link, HVAC, intelligent control, neural network, system identification.

Jason Teeter; Mo-yuen Chow; Senior Member

1998-01-01T23:59:59.000Z

295

Response surface optimization of an artificial neural network for predicting the size of re-assembled casein micelles  

Science Conference Proceedings (OSTI)

An artificial neural network (ANN) was designed to predict the size of re-assembled micelles in casein solutions as influenced by pH of solution and ultrasonic treatment. A generalized feed-forward network consisted of five neurons in the input layer, ... Keywords: Artificial neural network, Re-assembled casein micelles, Response surface method

Ashkan Madadlou; Zahra Emam-Djomeh; Mohamad Ebrahimzadeh Mousavi; Mohamadreza Ehsani; Majid Javanmard; David Sheehan

2009-10-01T23:59:59.000Z

296

The Use of a Hopfield Neural Network in Solving the Mobility Management Problem  

Science Conference Proceedings (OSTI)

This work presents a new approach to solve the location management problem by using the location areas approach. Hopfield Neural Network is used in this work to find the optimal configuration of location areas in a mobile network. Toward this end, the ...

Javid Taheri; Albert Y. Zomaya

2004-07-01T23:59:59.000Z

297

Nonlinear identification of a gasoline HCCI engine using neural networks coupled with principal component analysis  

Science Conference Proceedings (OSTI)

Homogeneous charge compression ignition (HCCI) is a futuristic combustion technology that operates with high efficiency and reduced emissions. HCCI combustion is characterized by complex nonlinear dynamics which necessitates the use of a predictive model ... Keywords: HCCI engine modeling, Multi-layer perceptron, Neural networks, Nonlinear system identification, Principal component analysis, Radial basis network

Vijay Manikandan Janakiraman; Xuanlong Nguyen; Dennis Assanis

2013-05-01T23:59:59.000Z

298

The application research of compound control based on the fuzzy neural network inverse method  

Science Conference Proceedings (OSTI)

Through the study of the boiler-turbine coordination and control network, this paper analysis the difficulties of the inverse system analytical method in practical use. A structure and learning method with close to the dynamic inverse system capacity ... Keywords: compound control, decoupling control instruction, inverse system, neural network

Qingli Wang; Yuanwei Jing; Lifu Wang; Zhi Kong

2009-06-01T23:59:59.000Z

299

Feed-forward and recurrent neural networks for source code informal information analysis  

Science Conference Proceedings (OSTI)

Design recovery, which is a part of the reverse engineering process of source code, must supply programmers with all the information they need to fully understand a program or a system. In this paper, a connectionist method that can be used for design ... Keywords: design recovery, feed-forward networks, informal information analysis, program understanding, recurrent neural networks

Ettore Merlo; Ian McAdam; Renato De Mori

2003-07-01T23:59:59.000Z

300

Design of QoS in Intelligent Communication Environments Based on Neural Network  

Science Conference Proceedings (OSTI)

Due to the latest developments in communication and computing, smart services and applications are being deployed for various applications such as entertainment, health care, smart homes, security and surveillance. In intelligent communication environments, ... Keywords: Congestion control, High-speed computer network, Intelligent communication environments, Neural network

N. Xiong; L. T. Yang; Y. Yang; J. H. Park; G. Wei

2011-01-01T23:59:59.000Z

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

A Global Model of $\\beta^-$-Decay Half-Lives Using Neural Networks  

E-Print Network (OSTI)

Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of $\\beta^-$-decay halflives of the class of nuclei that decay 100% by $\\beta^-$ mode in their ground states. A fully-connected multilayered feed forward network has been trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and cross-validation. The halflife estimates generated by the model are discussed and compared with the available experimental data, with previous results obtained with neural networks, and with estimates coming from traditional global nuclear models. Predictions of the new neural-network model are given for nuclei far from stability, with particular attention to those involved in r-process nucleosynthesis. This study demonstrates that in the framework of the $\\beta^-$-decay problem considered here, ...

Costiris, N; Gernoth, K A; Mavrommatis, E

2007-01-01T23:59:59.000Z

302

Application of Artificial Neural Networks to Complex Groundwater Management Problems  

SciTech Connect

As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models.

Coppola, Emery [NOAH LLC. (United States)], E-mail: noah.llc@mail.com; Poulton, Mary [University of Arizona, Department of Mining and Geological Engineering (United States); Charles, Emmanuel [U.S. Geological Survey (United States); Dustman, John [Summit EnviroSolutions (United States); Szidarovszky, Ferenc [University of Arizona, Department of Systems and Industrial Engineering (United States)

2003-12-15T23:59:59.000Z

303

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

NLE Websites -- All DOE Office Websites (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

304

Application of multi-layer recurrent neural network in chaotic time series prediction: a real case study of crude oil distillation capacity  

Science Conference Proceedings (OSTI)

A full customised case-oriented Multi-Layered Recurrent Neural Network (MLRNN) has been proposed to predict the Capacity of Crude Oil Distillation in OPEC Member Countries. Recurrent neural networks use feedback connections and have the potential to ...

Kaveh Khalili-Damghani; Soheil Sadi-Nezhad

2011-09-01T23:59:59.000Z

305

Fault diagnosis of steam turbine-generator sets using CMAC neural network approach and portable diagnosis apparatus implementation  

Science Conference Proceedings (OSTI)

Based on the vibration spectrum analysis, this paper proposed a CMAC (Cerebellar Model Articulation Controller) neural network diagnosis technique to diagnose the fault type of turbine-generator sets. This novel fault diagnosis methodology contains an ... Keywords: CMAC, PIC, fault diagnosis, microcontroller, neural network, turbine-generator sets

Chin-Pao Hung; Wei-Ging Liu; Hong-Zhe Su

2009-09-01T23:59:59.000Z

306

Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis  

Science Conference Proceedings (OSTI)

Detecting plant health condition is an important step in controlling disease and insect stress in agricultural crops. In this study, we applied neural network and principal components analysis techniques for discriminating and classifying different fungal ... Keywords: Derivative spectra, Neural networks, Principal component analysis, Remote sensing, Rice false smut disease, Rice glume blight disease, Spectral reflectance

Zhan-Yu Liu; Hong-Feng Wu; Jing-Feng Huang

2010-07-01T23:59:59.000Z

307

Artificial neural networks for predicting indoor temperature using roof passive cooling techniques in buildings in different climatic conditions  

Science Conference Proceedings (OSTI)

Three passive cooling methods (e.g. roof pond, reflective roof cooling and using insulation over the roof) have been experimentally evaluated using an experimental test structure. The objective of this work is to train an artificial neural network (ANN) ... Keywords: Artificial neural network, Energy saving, India, Passive cooling, Thermal comfort

Shrikant Pandey; D. A. Hindoliya; Ritu Mod

2012-03-01T23:59:59.000Z

308

Use of neural networks and decision fusion for lithostratigraphic correlation with sparse data, Mono-Inyo Craters, California  

Science Conference Proceedings (OSTI)

We explore the use of multiple artificial neural networks combined within the framework of the Dempster-Shafer Theory of Evidence to construct a hybrid information processing system for the correlation of tephra layers. The working hypothesis is that ... Keywords: Artificial neural network, California, Dempster-Shafer Theory, Mono-Inyo Craters, North mono eruption, Tephrastratigraphy, Tephrochronology, Theory of Evidence

M. Bursik; G. Rogova

2006-12-01T23:59:59.000Z

309

Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers  

Science Conference Proceedings (OSTI)

This study presents the comparison of various evolutionary computation (EC) optimization techniques applied to train the noise-injected multi-layer perceptron neural networks used for estimation of longitudinal dispersion coefficient in rivers. The special ... Keywords: Differential Evolution, Evolution Strategy, Evolutionary Computation, Longitudinal dispersion, Neural Networks, Noise injection, Particle Swarm Optimization

Adam P. Piotrowski; Pawel M. Rowinski; Jaroslaw J. Napiorkowski

2012-01-01T23:59:59.000Z

310

An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units  

Science Conference Proceedings (OSTI)

This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) and genetic algorithm clustering ensemble (GACE) for measuring efficiency as a complementary tool for the common techniques of the efficiency ... Keywords: Artificial neural network, Decision making units, Genetic algorithm, Performance assessment

A. Azadeh; M. Saberi; M. Anvari; M. Mohamadi

2011-04-01T23:59:59.000Z

311

Study on wave impact force prediction of different shore connecting structure based on improved BP neural network  

Science Conference Proceedings (OSTI)

In this paper, the advanced Neural Network technology was introduced to the field of the wave impact force prediction. A three-layered BP neural network is employed and the units of input layer are wave style, wave period, incident wave height, relative ... Keywords: BP, prediction, shore connecting structure, wave impact force

Xiaoguo Zhou; Shuguang Luan

2009-09-01T23:59:59.000Z

312

Methods of predicting milk yield in dairy cows-Predictive capabilities of Wood's lactation curve and artificial neural networks (ANNs)  

Science Conference Proceedings (OSTI)

The study is focused on the capability of artificial neural networks to forecast milk yield for both full and standardised lactations. We used a dataset of 108,931 daily milk yields (dataset A) collected from three lactations of dairy cows managed in ... Keywords: 305-d lactation, Artificial neural networks, Daily yields, Dairy cows, Wood's model

Wilhelm Grzesiak; Piotr B?aszczyk; René Lacroix

2006-12-01T23:59:59.000Z

313

Prediction of the index fund by Takagi-Sugeno fuzzy inference systems and feed-forward neural network  

Science Conference Proceedings (OSTI)

The paper presents (on the basis of passive investment strategies analysis) the design of the Takagi-Sugeno fuzzy inference system and the feed-forward neural network (with pre-processing of inputs time series) for prediction of the index fund. By means ... Keywords: Takagi-Sugeno fuzzy inference systems, feed-forward neural network, index fund, indicators of technical analysis, prediction

Vladír Olej

2006-02-01T23:59:59.000Z

314

Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: A feasibility study  

Science Conference Proceedings (OSTI)

An artificial neural network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting sleep spindles in band-pass filtered electroencephalograms (EEG), without feature extraction. Following optimum classification schemes, the ... Keywords: Artificial neural networks, EEG, Pattern recognition, Sleep spindles

Errikos M. Ventouras; Efstratia A. Monoyiou; Periklis Y. Ktonas; Thomas Paparrigopoulos; Dimitris G. Dikeos; Nikos K. Uzunoglu; Constantin R. Soldatos

2005-06-01T23:59:59.000Z

315

A novel extension neural network based partial discharge pattern recognition method for high-voltage power apparatus  

Science Conference Proceedings (OSTI)

This paper proposes a novel partial discharge (PD) pattern recognition method based on extension neural network (ENN) using fractal features. Five types of defect models are well-designed on the base of investigation of power apparatus failures. A PD ... Keywords: Extension distance, Extension neural network, Fractal feature, Partial discharge, Pattern recognition

Hung-Cheng Chen; Feng-Chang Gu; Meng-Hui Wang

2012-02-01T23:59:59.000Z

316

Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection  

Science Conference Proceedings (OSTI)

This paper presents an efficient and effective decision support system (DSS) for operational monitoring and control of water distribution systems based on a three layer General Fuzzy Min-Max Neural Network (GFMMNN) and graph theory. The operational monitoring ... Keywords: Decision support system, Graph theory, Loop corrective flows equations, Modeling and simulation, Neural network, Operational control of water distribution systems

Corneliu T. C. Arsene; Bogdan Gabrys; David Al-Dabass

2012-12-01T23:59:59.000Z

317

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

Science Conference Proceedings (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

318

The use of artificial neural network to predict exergetic performance of spray drying process: A preliminary study  

Science Conference Proceedings (OSTI)

A feedforward artificial neural network (ANN) was applied to predict the exergetic performance of a microencapsulation process via spray drying. The exergetic data was obtained from drying experiments conducted at different inlet drying air temperatures, ... Keywords: Artificial neural network (ANN), Exergetic performance, Multilayer perceptron (MLP), Spray drying process

Mortaza Aghbashlo; Hossien Mobli; Shahin Rafiee; Ashkan Madadlou

2012-10-01T23:59:59.000Z

319

The Analysis and Application of the Monitor Model of Gasifier Temperature Based on the PSO Neural Network  

Science Conference Proceedings (OSTI)

The coal gasification technology is widely used in industrial production, but in its production process, there exists a tough problem that the gasified temperature is not easy to detect and monitor. This paper proposes the approach of pso neural network, ... Keywords: particle swarm optimization, neural network, gasifier temperature

Qun Jia, Yongxin Li

2013-09-01T23:59:59.000Z

320

Development of an artificial neural network-based software for prediction of power plant canal water discharge temperature  

Science Conference Proceedings (OSTI)

Power plant cooling water systems that interact with nearby effluents are complex non-linear, large-time-delay systems. A neural network-based software tool was developed for prediction of the canal water discharge temperature at a coal-fired power plant ... Keywords: Canal water thermal discharge, Neural networks, Power plants

Carlos E. Romero; Jiefeng Shan

2005-11-01T23:59:59.000Z

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.


321

Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: A survey  

Science Conference Proceedings (OSTI)

Since neural networks have universal approximation capabilities, therefore it is possible to postulate them as solutions for given differential equations that define unsupervised errors. In this paper, we present a wide survey and classification of different ... Keywords: Backpropagation algorithm, Differential equations, Multilayer perceptron, Neural network, Radial basis functions

Manoj Kumar; Neha Yadav

2011-11-01T23:59:59.000Z

322

Learning images using compositional pattern-producing neural networks for source camera identification and digital demographic diagnosis  

Science Conference Proceedings (OSTI)

In this work, we propose a neural network based framework to explore the statistical correlation intrinsically embedded due to interpolations in a relatively small neighborhood, in which the interpolation process is cognized from the interpolation results ... Keywords: Camera identification, Demographics, Demosaicking, Inter-pixel correlation, Neural network

Qingyue Jin; Yizhen Huang; Na Fan

2012-03-01T23:59:59.000Z

323

Optimal Sizing of Energy Storage System in Solar Energy Electric Vehicle Using Genetic Algorithm and Neural Network  

Science Conference Proceedings (OSTI)

Owing to sun's rays distributing randomly and discontinuously and load fluctuation, energy storage system is very important in Solar Energy Electric Vehicle (SEEV). The combinatorial optimization by genetic algorithm and neural network was used to optimize ... Keywords: battery flywheel, genetic algorithm, neural network

Shiqiong Zhou; Longyun Kang; Miaomiao Cheng; Binggang Cao

2009-11-01T23:59:59.000Z

324

A novel approach for estimation of optimal embedding parameters of nonlinear time series by structural learning of neural network  

Science Conference Proceedings (OSTI)

In this work a novel approach for estimation of embedding parameters for reconstruction of underlying dynamical system from the observed nonlinear time series by a feedforward neural network with structural learning is proposed. The proposed scheme of ... Keywords: Chaos, Embedding parameters, Embedding theorem, Neural network, Nonlinear time series, Strange attractor, Structural learning

Yusuke Manabe; Basabi Chakraborty

2007-03-01T23:59:59.000Z

325

Numerical treatment for nonlinear MHD Jeffery-Hamel problem using neural networks optimized with interior point algorithm  

Science Conference Proceedings (OSTI)

In this paper new computational intelligence techniques have been developed for the nonlinear magnetohydrodynamics (MHD) Jeffery-Hamel flow problem using three different feed-forward artificial neural networks trained with an interior point method. The ... Keywords: Boundary value problems, Interior point method, Jeffery-Hamel Problem, Neural networks, Nonlinear ODEs, Radial basis function

Muhammad Asif Zahoor Raja, Raza Samar

2014-01-01T23:59:59.000Z

326

Using neural networks to enhance the Higgs boson signal at hadron colliders  

SciTech Connect

Neural networks are used to help distinguish the ZZ {yields} {ell}{sup +}{ell}{sup {minus}}-jet-jet signal produced by the decay of a 400 GeV Higgs boson at a proton-proton collider energy of 15 TeV from the ``ordinary`` QCD Z + jets background. The ideal case where only one event at a time enters the detector (no pile-up) and the case of multiple interactions per beam crossing (pile-up) are examined. In both cases, when used in conjunction with the standard cuts, neural networks provide an additional signal to background enhancement.

Field, R.D.; Kanev, Y.; Tayebnejad, M. [Univ. of Florida, Gainesville, FL (United States); Griffin, P.A. [Rockefeller Univ., New York, NY (United States)

1995-12-31T23:59:59.000Z

327

Novel delay-dependent asymptotic stability criteria for neural networks with time-varying delays  

Science Conference Proceedings (OSTI)

The problem of delay-dependent asymptotic stability criteria for neural networks (NNs) with time-varying delays is investigated. An improved linear matrix inequality based on delay-dependent stability test is introduced to ensure a large upper bound ... Keywords: 34K20, 34K25, 37C20, 37C25, 37C75, 37N35, 37N40, 93C10, 93C83, 93D05, 93D20, Delay-dependent, Linear matrix inequality (LMI), Neural networks (NNs), Time-varying delay

Junkang Tian; Dongsheng Xu; Jian Zu

2009-06-01T23:59:59.000Z

328

The Application of Visualization and Neural Network Techniques in A Power Transformer Condition Monitoring System  

E-Print Network (OSTI)

In this paper, visualization and neural network techniques are applied together to a power transformer condition monitoring system. Through visualizing the data from the chromatogram of oil-dissolved gases by 2-D and/or 3-D graphs, the potential failures of the power transformers become easy to be identified. Through employing some specific neural network techniques, the data from the chromatogram of oil-dissolved gases as well as those from the electrical inspections can be effectively analyzed. Experiments show that the described system works quite well in condition monitoring of power transformers.

Z.-H. Zhou; Y. Jiang; X.-R. Yin; S.-F. Chen; Zhi-hua Zhou; Yuan Jiang; Xu-ri Yin; Shi-fu Chen

2002-01-01T23:59:59.000Z

329

An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting  

Science Conference Proceedings (OSTI)

Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the ... Keywords: Bayesian model selection, Bayesian neural networks, Input selection, Load forecasting

Henrique S. Hippert; James W. Taylor

2010-04-01T23:59:59.000Z

330

A Study of Experimental Evaluations of Neural Network Learning Algorithms  

E-Print Network (OSTI)

articles of volume 6 (1993) and all 1 In this report, I will use the term evaluation to mean experimental evaluation. articles from numbers 1 to 5 of volume 7 (1994) were used. From Neural Computation, all articles of volume 5 (1993) and all articles from numbers 1 to 4 of volume 6 (1994) were used. The subsequent

Prechelt, Lutz

331

Selection of best neural network for estimating properties of diesel-biodiesel blends  

Science Conference Proceedings (OSTI)

Soybean oil was transesterified with methanol in the presence of alkaline catalyst to produce methyl esters commonly known as biodiesel. Biodiesel and diesel blends were prepared and tested in laboratory for flash point, fire point, viscosity and density. ... Keywords: artificial neural network, biodiesel, density, fire point, flash point, transesterification, viscosity

Jatinder Kumar; Ajay Bansal

2007-02-01T23:59:59.000Z

332

Fast static available transfer capability determination using radial basis function neural network  

Science Conference Proceedings (OSTI)

In a competitive electricity market, available transfer capability information is required by market participants as well as the system operator for secure operation of the power system. The on-line updating of available transfer capability information ... Keywords: Available transfer capability, Euclidean distance based clustering technique, Radial basis function neural network, Random forest technique

T. Jain; S. N. Singh; S. C. Srivastava

2011-03-01T23:59:59.000Z

333

Extended state estimator design method for neutral-type neural networks with time-varying delays  

Science Conference Proceedings (OSTI)

The problem of designing a state estimator having a global exponential convergence for a class of delayed neural networks of neutral-type is investigated in this paper. The time-delay pattern is a bounded differentiable time-varying function. The activation ...

Magdi Sadek Mahmoud

2012-03-01T23:59:59.000Z

334

Prediction of diesel engine performance using biofuels with artificial neural network  

Science Conference Proceedings (OSTI)

Biodiesel, bioethanol and biogas are the most important alternative fuels produced by using biologic origin sources. Effect of biofuel on engine performance is one of the research subjects of today. The engine experiments to test the engines are many ... Keywords: Artificial neural network, Biodiesel, Bioethanol, E-diesel, Engine performance

Hidayet O?uz; Ismail Sar?tas; Hakan Emre Baydan

2010-09-01T23:59:59.000Z

335

Sound-Based ranging system in greenhouse environment with multipath effect compensation using artificial neural network  

Science Conference Proceedings (OSTI)

In this study, sound-based ranging system in greenhouse environment with compensation of measurement error caused by multipath effect using Artificial Neural Network (ANN) was proposed. Greenhouse environment has special characteristic which is different ... Keywords: ANN, greenhouse, multipath effect, sound-based ranging system

Slamet Widodo; Tomoo Shiigi; Naing Min Than; Yuichi Ogawa; Naoshi Kondo

2012-11-01T23:59:59.000Z

336

Retrieval Property of Associative Memory Based on Inverse Function Delayed Neural Networks  

Science Conference Proceedings (OSTI)

Self-connection can enlarge the memory capacity of an associative memory based on the neural network. However, the basin size of the embedded memory state shrinks. The problem of basin size is related to undesirable stable states which are spurious. ... Keywords: associative memory, inverse function delayed model, retrieval dynamics, basin of attraction, negative resistance

Hongge Li; Yoshihiro Hayakawa; Koji Nakajima

2005-08-01T23:59:59.000Z

337

Comparative analysis of artificial neural networks and dynamic models as virtual sensors  

Science Conference Proceedings (OSTI)

This paper presents a comparison of predictive models for the estimation of engine power and tailpipe emissions for a 4kW gasoline scooter. This study forms a benchmark toward establishing an online emissions control and monitoring system to bring the ... Keywords: Artificial neural network, Backpropagation, Emissions predictive modeling, Optimization layer-by-layer, Virtual sensor

Wai Kean Yap; Vishy Karri

2013-01-01T23:59:59.000Z

338

Intelligent Experimental Design Using an Artificial Neural Network Meta Model and Information Theory  

Science Conference Proceedings (OSTI)

Ability to rapidly design products and their manufacturing process is a key to being competitive in a dynamic market environment. Traditional methods of design of experiment development are unsatisfactory when applied to design problems with large number ... Keywords: Artificial Neural Network, Experimental Design, Information Theory

Shi-Shang Jang; David Shan-Hill Wong; Junghui Chen

2006-05-01T23:59:59.000Z

339

A constructive neural network to predict pitting corrosion status of stainless steel  

Science Conference Proceedings (OSTI)

The main consequences of corrosion are the costs derived from both the maintenance tasks as from the public safety protection. In this sense, artificial intelligence models are used to determine pitting corrosion behaviour of stainless steel. This work ... Keywords: austenitic stainless steel, constructive neural networks, pitting corrosion

Daniel Urda, Rafael Marcos Luque, Maria Jesus Jiménez, Ignacio Turias, Leonardo Franco, José Manuel Jerez

2013-06-01T23:59:59.000Z

340

Prediction of spontaneous heating susceptibility of Indian coals using fuzzy logic and artificial neural network models  

Science Conference Proceedings (OSTI)

Coal mine fires due to spontaneous heating are a major concern worldwide. Most of these fires could be averted if suitable preventive measures are taken. Since the spontaneous heating potential of all types of coals are not the same, its accurate prediction ... Keywords: Artificial neural network, Coal, Crossing point temperature, Fuzzy expert system, Spontaneous heating, Sugeno model

H. B. Sahu; S. Padhee; S. S. Mahapatra

2011-03-01T23:59:59.000Z

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.


341

Environment Impact Evaluation of Coal Development Based on BP Neural Network  

Science Conference Proceedings (OSTI)

In order to evaluate the Environment impact of coal development, this paper selected evaluation indicators of the Environment impact of coal development according to certain principles and constructed Environment impact model of the coal development ... Keywords: Environment Impact, Coal Development, BP neural network

Kai Guo

2013-08-01T23:59:59.000Z

342

Analysis of Repetitive Flash Stimulation Frequencies and Record Periods to Detect Migraine Using Artificial Neural Network  

Science Conference Proceedings (OSTI)

Different kind of methods has been applied to detect the migraine by using flash stimulation. Especially frequency analysis of EEG signal is the most preferred method to detect the migraine by using flash stimulation. Different flash stimulation frequencies ... Keywords: Artificial neural network (ANN), Electroencephalography (EEG), Flash stimulation, Migraine

Selahaddin Batuhan Akben; Abdulhamit Subasi; Deniz Tuncel

2012-04-01T23:59:59.000Z

343

Loadability margin calculation of power system with SVC using artificial neural network  

Science Conference Proceedings (OSTI)

Voltage stability has become a major concern among the utilities over the past decade. With the development of FACTS devices, there is a growing interest in using these devices to improve the stability. In this paper a method using parallel self-organizing ... Keywords: Neural networks, Static var compensator, Voltage stability

P. K. Modi; S. P. Singh; J. D. Sharma

2005-09-01T23:59:59.000Z

344

Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach  

Science Conference Proceedings (OSTI)

An artificial neural network (ANN) approach was used to develop a new predictive model for the calculation of static formation temperature (SFT) in geothermal wells. A three-layer ANN architecture was successfully trained using a geothermal borehole ... Keywords: Artificial intelligence, Borehole drilling, Bottom-hole temperature, Geothermal energy, Horner method, Levenberg-Marquardt algorithm, Shut-in time

A. Bassam; E. Santoyo; J. Andaverde; J. A. Hernández; O. M. Espinoza-Ojeda

2010-09-01T23:59:59.000Z

345

Automatic fault diagnosis of internal combustion engine based on spectrogram and artificial neural network  

Science Conference Proceedings (OSTI)

This paper presents a signal analysis technique for internal combustion (IC) engine fault diagnosis based on the spectrogram and artificial neural network (ANN). Condition monitoring and fault diagnosis of IC engine through acoustic signal analysis is ... Keywords: acoustic analysis, fault diagnosis, internal combustion engine

Sandeep Kumar Yadav; Prem Kumar Kalra

2010-04-01T23:59:59.000Z

346

Research on Fish Intelligence for Fish Trajectory Prediction Based on Neural Network  

Science Conference Proceedings (OSTI)

This paper researches the behavior modes of some intelligent creature in some environment. The gained modes are used as movement models to construct NN to predict the moving trajectory and then catch it. Firstly the behavior patterns of fish that kept ... Keywords: Genetic algorithm, Intelligent robot, Neural network, Predicting trajectory, Visual servo

Yanmin Xue; Hongzhao Liu; Xiaohui Zhang; Mamoru Minami

2008-09-01T23:59:59.000Z

347

A granular neural network: Performance analysis and application to re-granulation  

Science Conference Proceedings (OSTI)

The multi-granularity problem is one of the key open problems in Granular Computing. Multiple descriptions of the same phenomena may use very different information granulations, complicating any comparison or synthesis of those descriptions. One method ... Keywords: Fuzzy logic, Granular computing, Linguistic hedges, Linguistic variables, Neural networks, Neuro-fuzzy systems

Scott Dick, Andrew Tappenden, Curtis Badke, Olufemi Olarewaju

2013-10-01T23:59:59.000Z

348

A neural network approach to audio-assisted movie dialogue detection  

Science Conference Proceedings (OSTI)

A novel framework for audio-assisted dialogue detection based on indicator functions and neural networks is investigated. An indicator function defines that an actor is present at a particular time instant. The cross-correlation function of a pair of ... Keywords: Cross-correlation, Cross-power spectral density, Dialogue detection, Indicator functions

Margarita Kotti; Emmanouil Benetos; Constantine Kotropoulos; Ioannis Pitas

2007-12-01T23:59:59.000Z

349

Non-linear Control of Heave for an Unmanned Helicopter Using a Neural Network  

Science Conference Proceedings (OSTI)

This paper describes a new non-linear control technique applied to the heave control of an unmanned rotorcraft. First a hybrid plant model consisting of exactly known dynamics is combined with a black-box representation of the unknown dynamics. Desired ... Keywords: Autonomous, Control, Helicopter, Neural network, Non-linear, UAV

Matthew Garratt; Sreenatha Anavatti

2012-06-01T23:59:59.000Z

350

A new probabilistic approach to on-line learning in artificial neural networks  

Science Conference Proceedings (OSTI)

In this paper, we introduce elements of probabilistic model that is suitable for modeling of learning algorithms in biologically plausible artificial neural networks framework. Model is based on two of the main concepts in quantum physics - a density ... Keywords: Hebbian learning rule, born rule, probabilistic PSA/PCA, von Neumann entropy

Marko V. Jankovic; Neil Rubens

2009-12-01T23:59:59.000Z

351

Performance control for manufacturing sustainability: a cellular neural network-based approach  

Science Conference Proceedings (OSTI)

The recent trends in optimisation of sustainability of production processes requires, amongst all the activities, a continuous detection and correction of process behaviours, monitoring those parameters critical to performance. Detection of special causes ... Keywords: associative memories, cellular neural networks, recognition process, statistical process control, sustainable manufacturing

Leonarda Carnimeo; Michele Dassisti

2012-04-01T23:59:59.000Z

352

Spike transmission on diverging/converging neural network and its implementation on a multilevel modeling platform  

Science Conference Proceedings (OSTI)

A multiple layers neural network characterized by diverging/converging projections between successive layers activated by an external spatio-temporal input pattern in presence of stochastic background activities was considered. In the previous studies ... Keywords: multilevel modeling, spatio-temporal firing patterns, synfire chain

Yoshiyuki Asai; Alessandro E. P. Villa

2012-09-01T23:59:59.000Z

353

2012 Special Issue: Neural networks and the experience and cultivation of mind  

Science Conference Proceedings (OSTI)

Hard core neural network research includes development of mathematical models of cognitive prediction and optimization aimed at dual use, both as models of what we see in brain circuits and behavior, and as useful general-purpose engineering technology. ... Keywords: Backpropagation, Cognitive optimization, Creativity, Human potential, Mirror neurons, Qi

Paul J. Werbos

2012-08-01T23:59:59.000Z

354

Generalization performance of support vector machines and neural networks in runoff modeling  

Science Conference Proceedings (OSTI)

Effective one-day lead runoff prediction is one of the significant aspects of successful water resources management in arid region. For instance, reservoir and hydropower systems call for real-time or on-line site-specific forecasting of the runoff. ... Keywords: Artificial neural networks (ANNs), Rainfall and climate data, Runoff prediction, Support vector machines (SVMs)

Mohsen Behzad; Keyvan Asghari; Morteza Eazi; Maziar Palhang

2009-05-01T23:59:59.000Z

355

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

Science Conference Proceedings (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

356

Identification and modeling for non-linear dynamic system using neural networks type MLP  

Science Conference Proceedings (OSTI)

In control systems, the model dynamics of linear systems is the principal and most important phase of a project, but when working with dynamic of non-linear systems obtain the model becomes a very complex task can be used techniques of system identification. ... Keywords: LP, algorithms, dynamic backprogation, modeling, multilayer perceptrons, neural networks dynamics, non-linear dynamics, training

Hernán González Acuña; Max Suell Dutra; Omar Lengerke

2009-06-01T23:59:59.000Z

357

A neural network approach to predicting price negotiation outcomes in business-to-business contexts  

Science Conference Proceedings (OSTI)

Price premiums are a key profit driver for long-term business relationships. For sellers in business-to-business (B2B) relationships, it is important to have appropriate strategies to negotiate price increases without trading off the relationships with ... Keywords: Business-to-business marketing, Neural network, Price negotiation, Regression analysis

Dirk C. Moosmayer; Alain Yee-Loong Chong; Martin J. Liu; Bjoern Schuppar

2013-06-01T23:59:59.000Z

358

Computational investigation of early child language acquisition using multimodal neural networks: a review of three models  

Science Conference Proceedings (OSTI)

Current opinion suggests that language is a cognitive process in which different modalities such as perceptual entities, communicative intentions and speech are inextricably linked. As such, the process of child language acquisition is one in which the ... Keywords: Child language acquisition, Computational model, Control system, Neural network

Abel Nyamapfene

2009-06-01T23:59:59.000Z

359

Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir  

Science Conference Proceedings (OSTI)

Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in numerous numbers of oil production applications like those in remote or unmanned locations topside exploitations that minimize platform space and subsea applications. ... Keywords: Artificial neural network, Evolutionary algorithms, Fuzzy logic, Hybrid, Imperialist competitive optimization, Oil flow rate

Mohammad Ali Ahmadi; Mohammad Ebadi; Amin Shokrollahi; Seyed Mohammad Javad Majidi

2013-02-01T23:59:59.000Z

360

Chaotic Time Series Forecasting Base on Fuzzy Adaptive PSO for Feedforward Neural Network Training  

Science Conference Proceedings (OSTI)

Short-term electricity demand forecasting for the next hour to several days out is one of the most important tools by which an electric utility plans and dispatches the loading of generating units in order to meet system demand. But there exists chaos ... Keywords: Particle Swarm Optimization (PSO), chaotic time Series, fuzzy system, feedforward neural network

Wenyu Zhang; Jinzhao Liang; Jianzhou Wang; Jinxing Che

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.


361

Neural network solution for intelligent service level agreement in e-health  

Science Conference Proceedings (OSTI)

In the next twenty years, service-oriented computing will play an important role in sharing the industry and the way business is conducted and services are delivered and managed. This paradigm is expected to have major impact on service economy; the ... Keywords: a QoS, e-health, neural network, service level agreements, service oriented architecture

Nada Al Salami, Sarmad Al Aloussi

2013-03-01T23:59:59.000Z

362

Diagnosis of hypoglycemic episodes using a neural network based rule discovery system  

Science Conference Proceedings (OSTI)

Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients' physiological parameters, corrected QT interval of the electrocardiogram ... Keywords: Genetic algorithm, Hypoglycemic episodes, Medical diagnosis, Neural networks, Type 1 diabetes mellitus

K. Y. Chan; S. H. Ling; T. S. Dillon; H. T. Nguyen

2011-08-01T23:59:59.000Z

363

Inferential Estimation of Texaco Coal Gasification Quality Using Stacked Neural Networks  

Science Conference Proceedings (OSTI)

The robust inferential estimation of syngas compositions using stacked neural network was presented. Data for building non-linear models is re-sampled using bootstrap techniques to form a number of sets of training and test data. For each data set, a ...

Rong Guo; Weiwei Guo; Dongchen Shi

2008-12-01T23:59:59.000Z

364

Stability analysis for the generalized Cohen-Grossberg neural networks with inverse Lipschitz neuron activations  

Science Conference Proceedings (OSTI)

In this paper, by using nonsmooth analysis approach, linear matrix inequality (LMI) technique, topological degree theory and Lyapunov-Krasovskii function method, the issue of global exponential stability is investigated for a class of generalized Cohen-Grossberg ... Keywords: Cohen-Grossberg neural networks, Global exponential stability, Inverse Lipschitz neuron activations, Linear matrix inequality, Nonsmooth behaved functions, Topological degree theory

Xiaobing Nie; Jinde Cao

2009-05-01T23:59:59.000Z

365

Artificial neural network control of a heat exchanger in a closed flow air circuit  

Science Conference Proceedings (OSTI)

This paper experimentally investigates the control of a heat exchanger in a closed flow air circuit. The temperature inside the test section of the test facility has been maintained at a set value by variation of air flow rate over the heat exchanger ... Keywords: Air circuit, Heat exchanger, Multi-layer perceptron, Neural network control, PID control

Kapil Varshney; P. K. Panigrahi

2005-07-01T23:59:59.000Z

366

Synthesis of Reinforcement Learning, Neural Networks, and PI Control Applied to a Simulated Heating Coil  

E-Print Network (OSTI)

integral (PI) controller, a neural network trained to predict the steady-state output of the PI controller learning, PI control, HVAC 1 Introduction Typical methods for designing fixed feedback controllers results in sub-optimal control performance. In many situations, the degree of uncertainty in the model

Kretchmar, R. Matthew

367

Sliding mode control of a hydrocarbon degradation in biopile system using recurrent neural network model  

Science Conference Proceedings (OSTI)

This paper proposes the use of a Recurrent Neural Network (RNN) for modeling a hydrocarbon degradation process carried out in a biopile system. The proposed RNN model represents a Kalman-like filter and it has seven inputs, five outputs and twelve neurons ...

Ieroham Baruch; Carlos-Roman Mariaca-Gaspar; Israel Cruz-Vega; Josefina Barrera-Cortes

2007-11-01T23:59:59.000Z

368

Venetian Blind Control System Based on Fuzzy Neural Network for Indoor Daylighting  

Science Conference Proceedings (OSTI)

For the indoor daylighting need, venetian blinds are a key element in the passive control of building’s vision environment. They help to control glare, daylighting, and overheating, all of which affect both the comfort of occupants and a building’s ... Keywords: fuzzy neural network, visual comfort, position of venetian blind, double control loops

Yifei Chen; Huai Li; Xueliang Chen

2009-12-01T23:59:59.000Z

369

An optimal tumor marker group-coupled artificial neural network for diagnosis of lung cancer  

Science Conference Proceedings (OSTI)

Background: Epidemiological statistics has shown that there are approximately 1.2 million new cases of lung cancer diagnosed every year and the death rate of these patients is 17.8%. Earlier diagnosis is key to promote the five-year survival rate of ... Keywords: Artificial neural network, Diagnosis, Lung cancer, Tumor marker

Yongjun Wu; Yiming Wu; Jing Wang; Zhen Yan; Lingbo Qu; Bingren Xiang; Yiguo Zhang

2011-09-01T23:59:59.000Z

370

A classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics  

Science Conference Proceedings (OSTI)

Objective:: This research work was aimed at developing a reliable screening device for diagnosis of heart murmurs in pediatrics. This is a significant problem in pediatric cardiology because of the high rate of incidence of heart murmurs in this population ... Keywords: Artificial neural network classifier, Pediatric cardiac auscultation, Phonocardiography

Sanjay R. Bhatikar; Curt DeGroff; Roop L. Mahajan

2005-03-01T23:59:59.000Z

371

Neural network based controller for Cr6+-Fe2+ batch reduction process  

Science Conference Proceedings (OSTI)

An automated pilot plant has been designed and commissioned to carry out online/real-time data acquisition and control for the Cr^6^+-Fe^2^+ reduction process. Simulated data from the Cr^6^+-Fe^2^+ model derived are validated with online data and laboratory ... Keywords: Batch system, Neural Networks, ORP, Redox process

Chew Chun Ming; M. A. Hussain; M. K. Aroua

2011-11-01T23:59:59.000Z

372

Fault identification in doubly fed induction generator using FFT and neural networks  

Science Conference Proceedings (OSTI)

This paper presents a fault identification system for doubly fed induction generator (DFIG). It considers cases of single phase short-circuits and load switching. The system uses the fast fourier transform (FFT) to preprocessor data, which consist by ... Keywords: fast fourier transform, fault identification, neural network

Marcelo Patrício de Santana; José Roberto Boffino de Almeida Monteiro; Geyverson Teixeira de Paula; Thales Eugenio Portes de Almeida; Gustavo Bueno Romero; Júlio César Faracco

2012-08-01T23:59:59.000Z

373

Near-surface air temperature estimation from ASTER data based on neural network algorithm  

Science Conference Proceedings (OSTI)

An algorithm based on the radiance transfer model (MODTRAN4) and a dynamic learning neural network for estimation of near-surface air temperature from ASTER data are developed in this paper. MODTRAN4 is used to simulate radiance transfer from the ground ...

K. B. Mao; H. J. Tang; X. F. Wang; Q. B. Zhou; D. L. Wang

2008-10-01T23:59:59.000Z

374

Integrating neural networks and logistic regression to underpin hyper-heuristic search  

Science Conference Proceedings (OSTI)

A hyper-heuristic often represents a heuristic search method that operates over a space of heuristic rules. It can be thought of as a high level search methodology to choose lower level heuristics. Nearly 200 papers on hyper-heuristics have recently ... Keywords: Data mining, Educational timetabling, Hyper-heuristic, Logistic regression, Neural network

Jingpeng Li; Edmund K. Burke; Rong Qu

2011-03-01T23:59:59.000Z

375

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

SciTech Connect

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

376

Natural language human-robot interface using evolvable fuzzy neural networks for mobile technology  

Science Conference Proceedings (OSTI)

In this paper, a human-robot speech interface for mobile technology is described which consists of intelligent mechanisms of human identification, speech recognition, word and command recognition, command meaning and effect analysis, command safety assessment, ... Keywords: artificial intelligence, human-robot interaction, hybrid neural networks, mobile technology, speech interface, voice communication

Wojciech Kacalak; Maciej Majewski

2009-09-01T23:59:59.000Z

377

Application of Neural Networks Optimized by Genetic Algorithms to Higgs Boson Search  

Science Conference Proceedings (OSTI)

This paper describe an application of a neural network approach to SM (standard model) and MSSM (minimal supersymetry standard model) Higgs search in the associated production ttH with H ¿ bb. This decay channel is considered as a discovery channel ...

Frantisek Hakl; Marek Hlavácek; Roman Kalous

2002-04-01T23:59:59.000Z

378

Optimizing neural network classifiers with ROOT on a rocks Linux cluster  

Science Conference Proceedings (OSTI)

We present a study to optimizemulti-layer perceptron (MLP) classification power with a Rocks Linux cluster [1]. Simulated data from a future high energy physics experiment at the Large Hadron Collider (LHC) is used to teach a neural network to separate ...

Tomas Lindén; Francisco García; Aatos Heikkinen; Sami Lehti

2006-06-01T23:59:59.000Z

379

Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence  

Science Conference Proceedings (OSTI)

The purpose of this study is to design a model to predict financial health of companies. Financial ratios for 180 manufacturing companies quoted in Tehran Stock Exchange for one year (year ended March 21, 2008) have been used. Three models; based on ... Keywords: Artificial neural networks, Discriminant analysis, Financial health prediction, Financial ratios, Genetic algorithm, Iranian company

F. Mokhatab Rafiei; S. M. Manzari; S. Bostanian

2011-08-01T23:59:59.000Z

380

The predictions of coal/char combustion rate using an artificial neural network approach  

E-Print Network (OSTI)

The predictions of coal/char combustion rate using an artificial neural network approach Q. Zhua of coal/char combustion was investigated. A database containing the combustion rate reactivity of 55 chars derived from 26 coals covering a wide range of rank and geographic origin was established to train

Thomas, Mark

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 to Predict Skeletal Metastasis in Patients with Prostate Cancer  

Science Conference Proceedings (OSTI)

The application of an artificial neural network (ANN) in prediction of outcomes using clinical data is being increasingly used. The aim of this study was to assess whether an ANN model is a useful tool for predicting skeletal metastasis in patients with ... Keywords: Artificial intelligence, Bone metastasis, Computer assisted, Image interpretation, Prostatic neoplasm, Radionuclide imaging

Jainn-Shiun Chiu; Yuh-Feng Wang; Yu-Cheih Su; Ling-Huei Wei; Jian-Guo Liao; Yu-Chuan Li

2009-04-01T23:59:59.000Z

382

A neural network-based approach for optimising rubber extrusion lines  

Science Conference Proceedings (OSTI)

The current study shows how data mining and artificial intelligence techniques can be used to introduce improvements in the rubber extrusion production process. One of the keys for planning manufacturing values is prior knowledge of the properties of ... Keywords: Cure characteristics, Data mining, Mixing conditions, Neural network, Rubber extrusion lines

A. González Marcos; A. V. Pernía Espinoza; F. Alba Elías; A. García Forcada

2007-12-01T23:59:59.000Z

383

Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs)  

Science Conference Proceedings (OSTI)

Autoregressive integrated moving average (ARIMA) models are one of the most important time series models applied in financial market forecasting over the past three decades. Improving forecasting especially time series forecasting accuracy is an important ... Keywords: Autoregressive integrated moving average (ARIMA), Hybrid models, Probabilistic neural networks (PNNs), Time series forecasting

Mehdi Khashei; Mehdi Bijari; Gholam Ali Raissi Ardali

2012-08-01T23:59:59.000Z

384

Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant  

Science Conference Proceedings (OSTI)

The energy efficiency of industrial plants is an important issue in any type of business but particularly in the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more as a means of reducing the amount ... Keywords: Cost optimization, Crude oil distillation, Data mining, Decision system, Expert system, Neural network, Petrochemical plant

Iñigo Monedero; Félix Biscarri; Carlos León; Juan I. Guerrero; Rocio González; Luis Pérez-Lombard

2012-08-01T23:59:59.000Z

385

Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations  

Science Conference Proceedings (OSTI)

A given observation in time series does not only depend on preceding one but also previous ones in general. Therefore, high order fuzzy time series approach might obtain better forecasts than does first order fuzzy time series approach. Defining fuzzy ... Keywords: Forecasting, Fuzzy relation, Fuzzy set, High order fuzzy time series, Neural networks

Cagdas H. Aladag; Murat A. Basaran; Erol Egrioglu; Ufuk Yolcu; Vedide R. Uslu

2009-04-01T23:59:59.000Z

386

Guidelines for Financial Forecasting with Neural Networks JingTao YAO  

E-Print Network (OSTI)

the observed movements. For instance, the forecasting of stock prices can be described in this way. Assume], foreign exchange rates forecasting [15, 24], option prices [25], advertising and sales volumes [13Guidelines for Financial Forecasting with Neural Networks JingTao YAO Dept of Information Systems

Yao, JingTao

387

A new feature selection algorithm and composite neural network for electricity price forecasting  

Science Conference Proceedings (OSTI)

In a competitive electricity market, the forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions. In this paper, a new forecast strategy is proposed ... Keywords: Composite neural network, Price forecast, Two stage feature selection technique

Farshid Keynia

2012-12-01T23:59:59.000Z

388

A novel hybridization of artificial neural networks and ARIMA models for time series forecasting  

Science Conference Proceedings (OSTI)

Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Both theoretical and empirical findings have indicated that integration of different models can be an effective ... Keywords: Artificial neural networks (ANNs), Auto-regressive integrated moving average (ARIMA), Hybrid models, Time series forecasting

Mehdi Khashei; Mehdi Bijari

2011-03-01T23:59:59.000Z

389

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

Indiana University

390

A fuzzy neural network based fault detection scheme for synchronous generator with internal fault  

Science Conference Proceedings (OSTI)

A fuzzy neural network (FNN) based inter-turn short circuit fault detection scheme for generator is proposed. The second harmonic magnitude of field current and the negative sequence components of voltages and currents are used as inputs for the FNN ...

Hongwei Fang; Changliang Xia

2009-08-01T23:59:59.000Z

391

A novel recurrent neural network-based prediction system for option trading and hedging  

Science Conference Proceedings (OSTI)

In order to reduce their exposure to the erratic fluctuations of the financial markets, traders are nowadays increasingly dealing with options and other derivative securities instead of directly trading in the underlying assets. This paradigm shift ... Keywords: Computational finance, Hedging system, Option trading, Recurrent neural network

C. Quek; M. Pasquier; N. Kumar

2008-10-01T23:59:59.000Z

392

Modelling of residual stresses in the shot peened material C-1020 by artificial neural network  

Science Conference Proceedings (OSTI)

This study consists of two cases: (i) The experimental analysis: Shot peening is a method to improve the resistance of metal pieces to fatigue by creating regions of residual stress. In this study, the residual stresses induced in steel specimen type ... Keywords: Artificial neural network, Layer removal technique, Residual stresses, Shot peening

Cetin Karata?; Adnan Sozen; Emrah Dulek

2009-03-01T23:59:59.000Z

393

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

Science Conference Proceedings (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

394

Form design of product image using grey relational analysis and neural network models  

Science Conference Proceedings (OSTI)

This paper presents a new approach to determining the best design combination of product form elements for matching a given product image represented by a word pair. A grey relational analysis (GRA) model is used to examine the relationship between product ... Keywords: Kansei Engineering, grey prediction, grey relational analysis, neural networks, product form, product image

Hsin-Hsi Lai; Yang-Cheng Lin; Chung-Hsing Yeh

2005-10-01T23:59:59.000Z

395

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

E-Print Network (OSTI)

implemented in software only. In the electro-optic layers, light emitting diodes (LEDs) are used to provide control circuitry and software. The LEDs are 680 nm peak light emitting diodes model A.N.D. Kilobright 180-optic neural-network to perform pattern analysis. The electro-optic layers each consist of 16 light emitting

Michel, Howard E.

396

Forecast of Weak Electrical Signals in Dahlia pinnata by Neural Networks  

Science Conference Proceedings (OSTI)

Signals of electrics in Dahlia pinnata were tested by a touching test system of self-made double shields with platinum sensors and tested data of electrical signals denoised by the wavelet soft threshold and also using Gaussian radial base function (RBF) ... Keywords: radial base function (RBF) neural network, wavelet soft threshold denoising, plant weak electrical signal, intelligent control, Dahlia pinnata

Lanzhou Wang; Jinli Ding

2010-05-01T23:59:59.000Z

397

Study of the behavior of a new boosting algorithm for recurrent neural networks  

Science Conference Proceedings (OSTI)

We present an algorithm for improving the accuracy of recurrent neural networks (RNNs) for time series forecasting. The improvement is achieved by combining a large number of RNNs, each of them is generated by training on a different set of examples. ...

Mohammad Assaad; Romuald Boné; Hubert Cardot

2005-09-01T23:59:59.000Z

398

Comparisons of Short Term Load Forecasting using Artificial Neural Network and Regression Method  

E-Print Network (OSTI)

In power systems the next day’s power generation must be scheduled every day, day ahead short-term load forecasting (STLF) is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. Under prediction of STLF leads to insufficient reserve capacity preparation and in turn, increases the operating cost by using expensive peaking units. On the other hand, over prediction of STLF leads to the unnecessarily large reserve capacity, which is also related to high operating cost. the research work in this area is still a challenge to the electrical engineering scholars because of its high complexity. How to estimate the future load with the historical data has remained a difficulty up to now, especially for the load forecasting of holidays, days with extreme weather and other anomalous days. With the recent development of new mathematical, data mining and artificial intelligence tools, it is potentially possible to improve the forecasting result. This paper presents a new neural network based approach for short-term load forecasting that uses the most correlated weather data for training, validating and testing the neural network. Correlation analysis of weather data determines the input parameters of the neural networks. And its results compare to regression method. Index terms Load Forecasting, artificial neural network, short term

Mr. Rajesh Deshmukh; Dr. Amita Mahor

2011-01-01T23:59:59.000Z

399

Designing cyclic pressure pulsing in naturally fractured reservoirs using an inverse looking recurrent neural network  

Science Conference Proceedings (OSTI)

In this paper, an inverse looking approach is presented to efficiently design cyclic pressure pulsing (huff 'n' puff) with N"2 and CO"2, which is an effective improved oil recovery method in naturally fractured reservoirs. A numerical flow simulation ... Keywords: Big Andy Field, CO2, Cyclic pressure pulsing, Huff 'n' puff, N2, Recurrent neural networks

E. Artun; T. Ertekin; R. Watson; B. Miller

2012-01-01T23:59:59.000Z

400

Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting  

SciTech Connect

Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework to incorporate the uncertainties associated with input, model structure, and parameter into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform the BNNs that only consider uncertainties associated with parameter and model structure. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters show that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of different uncertainty sources and including output error into the MCMC framework are expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting.

Zhang, Xuesong; Liang, Faming; Yu, Beibei; Zong, Ziliang

2011-11-09T23:59:59.000Z

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 novel fault diagnosis method based-on modified neural networks for photovoltaic systems  

Science Conference Proceedings (OSTI)

The main purpose of this paper is to propose an intelligent fault diagnostic method for photovoltaic (PV) systems. First, Solar Pro software package was used to simulate a photovoltaic system for gathering power generation data of photovoltaic modules ... Keywords: extension theory, fault diagnosis, matter-element model, neural networks, photovoltaic (PV) system

Kuei-Hsiang Chao; Chao-Ting Chen; Meng-Hui Wang; Chun-Fu Wu

2010-06-01T23:59:59.000Z

402

The development of a regional geomagnetic daily variation model using neural networks  

E-Print Network (OSTI)

The development of a regional geomagnetic daily variation model using neural networks P. R. Sutclie: 28 June 1999 / Accepted: 20 July 1999 Abstract. Global and regional geomagnetic ®eld models give the components of the geomagnetic ®eld as func- tions of position and epoch; most utilise a polynomial or Fourier

Paris-Sud XI, Université de

403

Neural Network Based Approaches, Solving Haplotype Reconstruction in MEC and MEC/GI Models  

Science Conference Proceedings (OSTI)

SNPs (Single Nucleotide Polymorphism) are different variant positions (1% of DNA sequence) of human genomes which their mutation is associated with complex genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary ... Keywords: Bioinformatics, biology and genomics, haplotype reconstruction, SNP fragments, clustering, genotype information, haplotype, reconstruction rate, unsupervised neural network

M-Hossein Moeinzadeh; Ehsan Asgarian; Sara Sharifian-R; Amir Najafi-Ardabili; Javad Mohammadzadeh

2008-05-01T23:59:59.000Z

404

Modeling and Experimental Research on Ground-Source Heat Pump in Operation by Neural Network  

Science Conference Proceedings (OSTI)

Ground source Heat Pump(GSHP) is becoming the more and more focus of the world¡¯s attention as a HVAC technique of energy saving and environment protection. This paper first introduced the experiment for Ground-Source water/water Heat Pump. The heat ... Keywords: Ground-Source Heat Pump(GSHP), Neural Network(NN) Predication modeling

Jianping Chen; Zhiwei Lian; Lizheng Tan; Weifeng Zhu; Weiqiang Zhang

2011-02-01T23:59:59.000Z

405

Estimation of seismic-induced demands on column splices with a neural network model  

Science Conference Proceedings (OSTI)

The current seismic design specification (AISC 341-05) requires that column splices in moment frames, when not made using complete joint penetration (CJP) welds, be designed to develop the flexural strength of the smaller connected column and the shear ... Keywords: Column splice, Neural network, Seismic design, Steel moment frame

Bulent Akbas; Jay Shen; Thomas A. Sabol

2011-12-01T23:59:59.000Z

406

Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study  

Science Conference Proceedings (OSTI)

Reliable performance evaluation of wastewater treatment plants (WWTPs) can be done by simulating the plant behavior over a wide range of influent disturbances, including series of rain events with different intensity and duration, seasonal temperature ... Keywords: Artificial neural networks, Modeling, Performance evaluation, Plant design, Simulation speed, Time series, Wastewater treatment plant

B. Ráduly; K. V. Gernaey; A. G. Capodaglio; P. S. Mikkelsen; M. Henze

2007-08-01T23:59:59.000Z

407

Genetic structure, consanguineous marriages and economic development: Panel cointegration and panel cointegration neural network analyses  

Science Conference Proceedings (OSTI)

Consanguineous marriages and their effects on human beings in light of biological effects of genetic sicknesses are discussed in many studies. Among many, the likelihood of sicknesses such as phenylketonuria, thalassemia, Landsteiner-Fanconi-Anderson's ... Keywords: Cansanguine marriage, Economic development, Economic growth, Human genetics, Panel cointegration MLP model, Panel data analysis, Panel neural network analysis

Melike Bildirici; Özgür Ömer Ersin; Meltem Kökdener

2011-05-01T23:59:59.000Z

408

Modular neural networks for recursive collaborative forecasting in the service chain  

Science Conference Proceedings (OSTI)

In order to honour customer demand and sustain quality of service in BT's service chain, accurate forecasting for customer demand is critical for optimal resource planning. In the more general context of service organisations, failure to allocate sufficient ... Keywords: Collaborative forecasting, Neural networks, Service chain

P. Stubbings; B. Virginas; G. Owusu; C. Voudouris

2008-08-01T23:59:59.000Z

409

Enhancing Cross-Correlation Analysis with Artificial Neural Networks for Nuclear Power Plant Feedwater Flow Measurement  

Science Conference Proceedings (OSTI)

One of the primary cost-saving objectives of the power plant industry, including the nuclear industry, has long been the efficient operation of plant systems. Since the maximum operating thermal power of any nuclear plant is bounded by the specific licensing ... Keywords: flow measurement, neural networks, nuclear power plant

Davide Roverso; Da Ruan

2004-05-01T23:59:59.000Z

410

CMSC 475/675 Introduction to Neural Networks Fall 2011 Project 2 Assignment  

E-Print Network (OSTI)

CMSC 475/675 Introduction to Neural Networks Fall 2011 Project 2 Assignment This project assignment') is their Manhattan distance. For example, the distance between (1, 2) and (2, 2) is 1, between (1, 2) and (2, 1) is 2 vectors on the 3 by 3 output grid. You can use any language for this project. Report Besides

Peng, Yun

411

Prediction of financial information manipulation by using support vector machine and probabilistic neural network  

Science Conference Proceedings (OSTI)

Different methods have been used to predict financial information manipulation that can be defined as the distortion of the information in the financial statements. The purpose of this paper is to predict financial information manipulation by using support ... Keywords: Financial information manipulation, Probabilistic neural network, Support vector machine

Hulisi Ö?üt; Ramazan Akta?; Ali Alp; M. Mete Do?anay

2009-04-01T23:59:59.000Z

412

Neural network modeling of pulsed-laser weld pool shapes in aluminum alloy welds  

SciTech Connect

A model was developed to predict the weld pool shape in pulsed Nd:YAG laser welds of aluminum alloy 5754. The model utilized neural network analysis to relate the weld process conditions to four pool shape parameters: penetration, width, width at half-penetration, and cross-sectional area. The model development involved the identification of the input (process) variables, the desired output (shape) variables, and the optimal neural network architecture. The latter was influenced by the number of defined inputs and outputs as well as the amount of data that was available for training the network. After appropriate training, the best network was identified and was used to predict the weld shape. A routine to convert the shape parameters into predicted weld profiles was also developed. This routine was based on the actual experimental weld profiles and did not impose an artificial analytical function to describe the weld profile. The neural network model was tested on experimental welds. The model predictions were excellent. It was found that the predicted shapes were within the experimental variations that were found along the length of the welds (due to the pulsed nature of the weld power) and the reproducibility of welds made under nominally identical conditions.

Vitek, J.M.; Iskander, Y.S.; Oblow, E.M.; Babu, S.S.; David, S.A. [Oak Ridge National Lab., TN (United States); Fuerschbach, P.W. [Sandia National Labs., Albuquerque, NM (United States); Smartt, H.B.; Pace, D.P. Tolle, C.R. [Idaho National Engineering and Environmental Lab., Idaho Falls, ID (United States)

1998-11-01T23:59:59.000Z

413

A Global Model of $?^-$-Decay Half-Lives Using Neural Networks  

E-Print Network (OSTI)

Statistical modeling of nuclear data using artificial neural networks (ANNs) and, more recently, support vector machines (SVMs), is providing novel approaches to systematics that are complementary to phenomenological and semi-microscopic theories. We present a global model of $\\beta^-$-decay halflives of the class of nuclei that decay 100% by $\\beta^-$ mode in their ground states. A fully-connected multilayered feed forward network has been trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and cross-validation. The halflife estimates generated by the model are discussed and compared with the available experimental data, with previous results obtained with neural networks, and with estimates coming from traditional global nuclear models. Predictions of the new neural-network model are given for nuclei far from stability, with particular attention to those involved in r-process nucleosynthesis. This study demonstrates that in the framework of the $\\beta^-$-decay problem considered here, global models based on ANNs can at least match the predictive performance of the best conventional global models rooted in nuclear theory. Accordingly, such statistical models can provide a valuable tool for further mapping of the nuclidic chart.

N. Costiris; E. Mavrommatis; K. A. Gernoth; J. W. Clark

2007-01-31T23:59:59.000Z

414

Neural Network Input Representations that Produce Accurate Consensus Sequences from DNA Fragment Assemblies  

E-Print Network (OSTI)

Motivation: Given inputs extracted from an aligned column of DNA bases and the underlying Perkin Elmer Applied Biosystems (ABI) fluorescent traces, our goal is to train a neural network to correctly determine the consensus base for the column. Choosing an appropriate network input representation is critical to success in this task. We empirically compare five representations; one uses only base calls and the others include trace information. Results: We attained the most accurate results from networks that incorporate trace information into their input representations. Based on estimates derived from using 10-fold cross-validation, the best network topology produces consensus accuracies ranging from 99.26% to over 99.98% for coverages from two to six aligned sequences. With a coverage of six, it makes only three errors in 20,000 consensus calls. In contrast, the network that only uses base calls in its input representation has over double that error rate -- eight errors in 20,000 cons...

C.F. Allex; J.W. Shavlik; F.R. Blattner

1999-01-01T23:59:59.000Z

415

Enhancing the Authentication of Bank Cheque Signatures by Implementing Automated System Using Recurrent Neural Network  

E-Print Network (OSTI)

The associatie memory feature of the Hopfield type recurrent neural network is used for the pattern storage and pattern authentication.This paper outlines an optimization relaxation approach for signature verification based on the Hopfield neural network (HNN)which is a recurrent network.The standard sample signature of the customer is cross matched with the one supplied on the Cheque.The difference percentage is obtained by calculating the different pixels in both the images.The network topology is built so that each pixel in the difference image is a neuron in the network.Each neuron is categorized by its states,which in turn signifies that if the particular pixel is changed.The network converges to unwavering condition based on the energy function which is derived in experiments.The Hopfield's model allows each node to take on two binary state values (changed/unchanged)for each pixel.The performance of the proposed technique is evaluated by applying it in various binary and gray scale images.This paper con...

Rao, Mukta; Dhaka, Vijaypal Singh

2010-01-01T23:59:59.000Z

416

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

Science Conference Proceedings (OSTI)

This paper evaluates the applicability of neural networks to estimate wind speeds at various target locations using neighboring reference locations on the South coast of Newfoundland Canada. The stations were chosen to cover a variety of ...

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

417

Generation of optimal artificial neural networks using a pattern search algorithm: Application to approximation of chemical systems  

Science Conference Proceedings (OSTI)

A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that ...

Matthias Ihme; Alison L. Marsden; Heinz Pitsch

2008-02-01T23:59:59.000Z

418

Neural Network Based Modeling of a Large Steam Turbine-Generator Rotor Body Parameters from On-Line Disturbance Data  

E-Print Network (OSTI)

Neural Network Based Modeling of a Large Steam Turbine-Generator Rotor Body Parameters from On technique to estimate and model rotor- body parameters of a large steam turbine-generator from real time

419

Estimation of Subsurface Temperature Anomaly in the North Atlantic Using a Self-Organizing Map Neural Network  

Science Conference Proceedings (OSTI)

A self-organizing map (SOM) neural network was developed from Argo gridded datasets in order to estimate a subsurface temperature anomaly (STA) from remote sensing data. The SOM maps were trained using anomalies of sea surface temperature (SST), ...

Xiangbai Wu; Xiao-Hai Yan; Young-Heon Jo; W. Timothy Liu

2012-11-01T23:59:59.000Z

420

Neural Network Training for Prediction of Climatological Time Series, Regularized by Minimization of the Generalized Cross-Validation Function  

Science Conference Proceedings (OSTI)

Neural network (NN) training is the optimization process by which the relation between the NN input and output is established. A new formulation for the NN training is presented where an NN model is reconstructed such that it produces predicted ...

Yuval

2000-05-01T23:59:59.000Z

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.


421

Gaussian sum approach with optimal experiment design for neural network  

Science Conference Proceedings (OSTI)

System identification is a discipline for construction of mathematical models of stochastic systems based on measured experimental data. Significant role in the system identification plays a selection of input signal which influences quality of obtained ... Keywords: multi-layer perceptron network, nonlinear parameters estimation, optimal experiment design, probability density function, system identification

Pavel Hering; Miroslav Šimandl

2007-08-01T23:59:59.000Z

422

A Draughts Learning System Based on Neural Networks and Temporal Differences: The Impact of an Efficient Tree-Search Algorithm  

Science Conference Proceedings (OSTI)

The NeuroDraughts is a good automatic draughts player which uses temporal difference learning to adjust the weights of an artificial neural network whose role is to estimate how much the board state represented in its input layer by NET-FEATUREMAP is ... Keywords: Alpha-Beta Pruning, Automatic Learning, Checkers, Draughts, Iterative Deepening, Neural Network, Table Hashing, Temporal Difference Learning, Transposition Table, Zobrist Key

Gutierrez Soares Caixeta; Rita Maria Silva Julia

2008-10-01T23:59:59.000Z

423

Prediction of solubility of gases in polystyrene by Adaptive Neuro-Fuzzy Inference System and Radial Basis Function Neural Network  

Science Conference Proceedings (OSTI)

Adaptive Neuro-Fuzzy Inference System (ANFIS) and Radial Basis Function Neural Network (RBF NN) have been developed for prediction of solubility of various gases in polystyrene. Solubility of butane, isobutene, carbon dioxide, 1,1,1,2-tetrafluoroethane ... Keywords: ANFIS, ANN, ARD, Adaptive Neuro-Fuzzy Inference System (ANFIS), BP, HCFC-142b, HFC-134a, HFC-l52a, MLP, PS, Polystyrene, RBF NN, Radial Basis Function Neural Network (RBF NN), S-L EOS, Solubility

Aboozar Khajeh; Hamid Modarress

2010-04-01T23:59:59.000Z

424

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

NLE Websites -- All DOE Office Websites (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

425

A Neural Network Model for Construction Projects Site Overhead Cost Estimating in Egypt  

E-Print Network (OSTI)

Estimating of the overhead costs of building construction projects is an important task in the management of these projects. The quality of construction management depends heavily on their accurate cost estimation. Construction costs prediction is a very difficult and sophisticated task especially when using manual calculation methods. This paper uses Artificial Neural Network (ANN) approach to develop a parametric cost-estimating model for site overhead cost in Egypt. Fifty-two actual real-life cases of building projects constructed in Egypt during the seven year period 2002-2009 were used as training materials. The neural network architecture is presented for the estimation of the site overhead costs as a percentage from the total project price.

ElSawy, Ismaail; Razek, Mohammed Abdel

2011-01-01T23:59:59.000Z

426

Internal leakage detection for feedwater heaters in power plants using neural networks  

Science Conference Proceedings (OSTI)

As interest in safety and performance of power plants becomes more serious and wide-ranging, the significance of research on turbine cycles has attracted more attention. This paper particularly focuses on thermal performance analysis under the conditions ... Keywords: CBM, COP, DCA, DP, Diagnosis, FWBP, FWH, FWP, Feedwater heater, HP FWH, HP TBN, Internal leakage, LP FWH, LP TBN, Neural network, PEPSE, SG, TD, TTD, Thermal performance, Turbine cycle, VWO

Gyunyoung Heo; Song Kyu Lee

2012-04-01T23:59:59.000Z

427

Neural network predictions for Z' boson within LEP2 data set of Bhabha process  

E-Print Network (OSTI)

The neural network approach is applied to search for the Z'-boson within the LEP2 data set for e+ e- -> e+ e- scattering process. In the course of the analysis, the data set is reduced by 20 percent. The axial-vector and vector couplings of the Z' are estimated at 95 percent CL within a two-parameter fit. The mass is determined to be 0.53-1.05 TeV. Comparisons with other results are given.

A. N. Buryk; V. V. Skalozub

2008-02-11T23:59:59.000Z

428

Estimation of the Thickness of Overlapping Materials by Using Neural Networks  

Science Conference Proceedings (OSTI)

Backpropagation type artificial neural networks (ANN) were used to estimate the thickness of two overlapping materials by inspecting two X-ray images obtained at different x-ray tube voltage settings. Radiographic images of overlapping aluminum and brass wedges were simulated by using the X-ray radiography simulation program XRSIM. Simulated images were used for training and testing of the ANNs. The average estimation error was less than 4% and 7% on the training and test data respectively.

Reen, N.; Tansel, I. N.; Chen, P.; Wang, X. [Florida International University, Miami, Fl 33174 (United States); Inanc, F. [CNDE, Iowa State University, Ames, IA 50011-3042 (United States); Kropas-Hughes, C. [AFRL/MLLP, Wright Patterson, OH 45433 (United States)

2005-04-09T23:59:59.000Z

429

Exponential stability preservation in discrete-time analogues of artificial neural networks with distributed delays  

Science Conference Proceedings (OSTI)

This paper demonstrates that there is a discrete-time analogue which does not require any restriction on the size of the time-step in order to preserve the exponential stability of an artificial neural network with distributed delays. The analysis exploits ... Keywords: 34K28, 39A11, 39A12, 92B20, Discrete-time analogues, Distributed delays, Exponential stability, Halanay inequalities, Lyapunov sequences

Sannay Mohamad

2008-05-01T23:59:59.000Z

430

Using artificial neural networks to predict the performance of a liquid metal reflux solar receiver: Preliminary results  

DOE Green Energy (OSTI)

Three and four-layer backpropagation artificial neural networks have been used to predict the power output of a liquid metal reflux solar receiver. The networks were trained using on-sun test data recorded at Sandia National Laboratories in Albuquerque, New Mexico. The preliminary results presented in this paper are a comparison of how different size networks train on this particular data. The results give encouragement that it will be possible to predict output power of a liquid metal receiver under a variety of operating conditions using artificial neural networks.

Fowler, M.M. [North Carolina Agricultural and Technical State Univ., Greensboro, NC (United States). Dept. of Mechanical Engineering

1995-12-31T23:59:59.000Z

431

Neural Networks as a Composition Diagnostic for Ultra-high Energy Cosmic Rays  

E-Print Network (OSTI)

We analyze here the possibility of studying mass composition in the Auger data sample using neural networks as a diagnostic tool. Extensive air showers were simulated using the AIRES code, for the two hadronic interaction models in current use: QGSJet and Sibyll. Both, photon and hadron primaries were simulated and used to generate events. The output parameters from the ground array were simulated for the typical instrumental and environmental conditions at the Malarg\\"ue Auger site using the code SAMPLE. Besides photons, hydrogen, helium, carbon, oxygen, magnesium, silicon, calcium and iron nuclei were also simulated. We show that Principal Components Analysis alone is enough to separate individual photon from hadron events, but the same technique cannot be applied to the classification of hadronic events. The latter requires the use of a more robust diagnostic. We show that neural networks are potentially powerful enough to discriminate proton from iron events almost on an event-by-event basis. However, in the case of a more realistic multi-component mixture of primary nuclei, only a statistical estimate of the average mass can be reliably obtained. Although hybrid events are not explicitly simulated, we show that, whenever hybrid information in the form of $X_{max}$ is introduced in the training procedure of the neural networks, a considerable improvement can be achieved in mass discrimination analysis.

Andre K. O. Tiba; Gustavo A. Medina-Tanco; Sergio J. Sciutto

2005-02-13T23:59:59.000Z

432

Bayesian Neural Networks for Uncertainty Analysis of Hydrologic Modeling: A Comparison of Two Schemes  

SciTech Connect

Bayesian Neural Networks (BNNs) have been shown as useful tools to analyze modeling uncertainty of Neural Networks (NNs). This research focuses on the comparison of two BNNs. The first BNNs (BNN-I) use statistical methods to describe the characteristics of different uncertainty sources (input, parameter, and model structure) and integrate these uncertainties into a Markov Chain Monte Carlo (MCMC) framework to estimate total uncertainty. The second BNNs (BNN-II) lump all uncertainties into a single error term (i.e. the residual between model prediction and measurement). In this study, we propose a simple BNN-II, which use Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) to calibrate Neural Networks with different structures (number of hidden units) and combine the predictions from different NNs to derive predictions and uncertainty analysis. We tested these two BNNs in two watersheds for daily and monthly hydrologic simulation. The BMA based BNNs developed in this study outperforms BNN-I in the two watersheds in terms of both accurate prediction and uncertainty estimation. These results show that, given incomplete understanding of the characteristics associated with each uncertainty source, the simple lumped error approach may yield better prediction and uncertainty estimation.

Zhang, Xuesong; Zhao, Kaiguang

2012-06-01T23:59:59.000Z

433

A Brief History of Excitable Map-Based Neurons and Neural Networks  

E-Print Network (OSTI)

This review gives a short historical account of the excitable maps approach for modeling neurons and neuronal networks. Some early models, due to Pasemann (1993), Chialvo (1995) and Kinouchi and Tragtenberg (1996), are compared with more recent proposals by Rulkov (2002) and Izhikevich (2003). We also review map-based schemes for electrical and chemical synapses and some recent findings as critical avalanches in map-based neural networks. We conclude with suggestions for further work in this area like more efficient maps, compartmental modeling and close dynamical comparison with conductance-based models.

M. Girardi-Schappo; M. H. R. Tragtenberg; O. Kinouchi

2013-03-01T23:59:59.000Z

434

Data driven process monitoring based on neural networks and classification trees  

E-Print Network (OSTI)

Process monitoring in the chemical and other process industries has been of great practical importance. Early detection of faults is critical in avoiding product quality deterioration, equipment damage, and personal injury. The goal of this dissertation is to develop process monitoring schemes that can be applied to complex process systems. Neural networks have been a popular tool for modeling and pattern classification for monitoring of process systems. However, due to the prohibitive computational cost caused by high dimensionality and frequently changing operating conditions in batch processes, their applications have been difficult. The first part of this work tackles this problem by employing a polynomial-based data preprocessing step that greatly reduces the dimensionality of the neural network process model. The process measurements and manipulated variables go through a polynomial regression step and the polynomial coefficients, which are usually of far lower dimensionality than the original data, are used to build a neural network model to produce residuals for fault classification. Case studies show a significant reduction in neural model construction time and sometimes better classification results as well. The second part of this research investigates classification trees as a promising approach to fault detection and classification. It is found that the underlying principles of classification trees often result in complicated trees even for rather simple problems, and construction time can excessive for high dimensional problems. Fisher Discriminant Analysis (FDA), which features an optimal linear discrimination between different faults and projects original data on to perpendicular scores, is used as a dimensionality reduction tool. Classification trees use the scores to separate observations into different fault classes. A procedure identifies the order of FDA scores that results in a minimum tree cost as the optimal order. Comparisons to other popular multivariate statistical analysis based methods indicate that the new scheme exhibits better performance on a benchmarking problem.

Zhou, Yifeng

2004-08-01T23:59:59.000Z

435

A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization  

Science Conference Proceedings (OSTI)

The solution of the inverse kinematics problem is fundamental in robot control. Many traditional inverse kinematics problem solutions, such as the geometric, iterative, and algebraic approaches, are inadequate for redundant robots. Recently, much attention ... Keywords: 6-Degree-of-freedom robot, Elman networks, Genetic algorithms, Inverse kinematics problem, Neural networks, Robotics

Ra?It KöKer

2013-02-01T23:59:59.000Z

436

A new void fraction correlation inferred from artificial neural networks for modeling two-phase flows in geothermal wells  

Science Conference Proceedings (OSTI)

A new empirical void fraction correlation was developed using artificial neural network (ANN) techniques. The artificial networks were trained using the backpropagation algorithm and production data obtained from a worldwide database of geothermal wells. ... Keywords: Artificial intelligence, Geothermal energy, Liquid holdup, Pressure gradients, Simulation, Statistics

A. Álvarez del Castillo; E. Santoyo; O. Garcí a-Valladares

2012-04-01T23:59:59.000Z

437

Neural Network-Based Classification of Single-Phase Distribution Transformer Fault Data  

E-Print Network (OSTI)

The ultimate goal of this research is to develop an online, non-destructive, incipient fault detection system that is able to detect incipient faults in transformers and other electric equipment before the faults become catastrophic. With the condition assessment capability of the detection system, operators are equipped with better information during their decision-making process. Corrective actions are taken prior to transformer and equipment failures to prevent down-time and reduce operating and maintenance costs. Diagnosis of data associated with incipient failures is essential to develop an efficient, non-destructive, and online system. Field testing data were collected from controlled experiment and simulation data from mathematical models are studied. This thesis presents a data-mining approach to analyze field recorded and simulation data to characterize incipient fault data and study its properties. A supervised classifier using neural network (NN) toolbox in Matlab provides an efficient and accurate classification method to separate monitoring signal data into clusters base on their properties. However, raw data collected from the field and simulations will create too many dimensions and inputs to the neural network and make it a complex and over-generalized classification. Therefore, features are extracted from the data set, and these features are formed into feature clusters in order to identify patterns in signals as they are related to various physical behaviors of the system. The similarity between recognized patterns and patterns shown in future monitoring signals will trigger the warning of initializing or developing faults in transformers or equipment. This thesis demonstrates how different features were extracted from the raw data using various analysis techniques in both time domain and time-frequency domain, and the design and implementation of a neural network-based classification method. The classifier outputs are classes of data being separated into groups based on their characteristics and behaviors. Meaning of different classes is also explained in this thesis.

Zhang, Xujia

2006-08-16T23:59:59.000Z

438

Classification of parameter changes in a dynamic system with the use of wavelet analysis and neural networks  

Science Conference Proceedings (OSTI)

In this paper a neural detector of internal parameter changes in a stationary, non-linear SISO dynamic system is considered. A dynamic system is usually described by an input-output relation or by a set of state equations. Each change of parameter values ... Keywords: Detection of current states of a dynamic system, Discrete wavelet decomposition, Kohonen network, LVQ neural classifier, Time-frequency transforms, Vector quantisation

Ewa Swiercz

2012-03-01T23:59:59.000Z

439

Comparative evaluation of neural-network-based and PI current controllers for HVDC transmission  

Science Conference Proceedings (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

440

CHF detection using spatio-temporal neural network and wavelet transform  

SciTech Connect

In most CHF experiments, the CHF detection is usually accomplished by measurement of temperature using thermocouples, resistance temperature device (RTD), etc. there is some ambiguity of human subjectivity in the experimental decision of CHF occurrence. This judgment can cause lack of consistency and objectivity in experiments. In this regard, the authors investigate the CHF condition, especially the LPLF condition. From the investigation of the CHF condition and conventional definition of the CHF, they develop the temperature pattern recognition systems, which are able to detect the CHF occurrence. The CHF patterns are recognized using spatiotemporal neural network (STN) and wavelet transform. Each CHF detection method shows good agreement with human decision.

Kim, S.H.; Bang, I.C.; Baek, W.P.; Chang, S.H.; Moon, S.K.

2000-02-01T23:59:59.000Z

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.


441

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

442

Neural network system and methods for analysis of organic materials and structures using spectral data  

DOE Patents (OSTI)

Apparatus and processes 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, Bernd J. (Athens, GA); Sellers, Jeffrey P. (Suwanee, GA); Thomsen, Jan U. (Fredricksberg, DK)

1993-01-01T23:59:59.000Z

443

Formal verification of wastewater treatment processes using events detected from continuous signals by means of artificial neural networks. Case study: SBR plant  

Science Conference Proceedings (OSTI)

This paper proposes a modular architecture for the analysis and the validation of wastewater treatment processes. An algorithm using neural networks is used to extract the relevant qualitative patterns, such as ''apexes'', ''knees'' and ''steps'', from ... Keywords: Artificial neural networks, Business process management, Event detection, Intelligent systems, Rule-based management system, SBR

Luca Luccarini; Gianni Luigi Bragadin; Gabriele Colombini; Maurizio Mancini; Paola Mello; Marco Montali; Davide Sottara

2010-05-01T23:59:59.000Z

444

Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete  

Science Conference Proceedings (OSTI)

In this study, an artificial neural network (ANN) and fuzzy logic (FL) study were developed to predict the compressive strength of silica fume concrete. A data set of a laboratory work, in which a total of 48 concretes were produced, was utilized in ... Keywords: Compressive strength, Concrete, Fuzzy logic, Neural networks, Silica fume

Fatih Özcan; Cengiz D. Ati?; Okan Karahan; Erdal Uncuo?lu; Harun Tanyildizi

2009-09-01T23:59:59.000Z

445

Evaluation of global and local training techniques over feed-forward neural network architecture spaces for computer-aided medical diagnosis  

Science Conference Proceedings (OSTI)

In this paper, we investigate the performance of global vs. local techniques applied to the training of neural network classifiers for solving medical diagnosis problems. The presented methodology of the investigation involves systematic and exhaustive ... Keywords: Artificial neural networks, Backpropagation, Particle swarm optimization

Turker Ince; Serkan Kiranyaz; Jenni Pulkkinen; Moncef Gabbouj

2010-12-01T23:59:59.000Z

446

The use of the Taguchi method with grey relational analysis and a neural network to optimize a novel GMA welding process  

Science Conference Proceedings (OSTI)

The objective of this paper is to present an integrated approach using the Taguchi method (TM), grey relational analysis (GRA) and a neural network (NN) to optimize the weld bead geometry in a novel gas metal arc (GMA) welding process. The TM is first ... Keywords: Gas metal arc welding, Grey relational analysis, Neural networks, Taguchi method

Hsuan-Liang Lin

2012-10-01T23:59:59.000Z

447

Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil  

Science Conference Proceedings (OSTI)

This work presents an application of probabilistic neural networks to map the potential for platinum group elements (PGE) mineralization sites in the northeast portion of the Carajas Mineral Province (CMP), Brazilian Amazon. Geological and geophysical ... Keywords: Carajás Mineral Province, Leave-one-out test, Mineral potential mapping, Probabilistic neural network

Emilson Pereira Leite; Carlos Roberto de Souza Filho

2009-03-01T23:59:59.000Z

448

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey)  

Science Conference Proceedings (OSTI)

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat-Turkey). Digital elevation model (DEM) was first constructed ... Keywords: Artificial neural networks, Frequency ratio, GIS, Kat (Tokat-Turkey), Landslide, Logistic regression, Susceptibility map

I??k Yilmaz

2009-06-01T23:59:59.000Z

449

Web Version of the Artificial Neural Network Short Term Load Forecaster (WebANNSTLF 6.0)  

Science Conference Proceedings (OSTI)

The EPRI-developed ANNSTLF (Artificial Neural Network Short-Term Load Forecaster) is a neural-network load forecasting software system that uses historical load and weather parameters to predict future load values. EPRI has upgraded the most recent desktop version of the software (ANNSTLF 5.1) to a web-based version (WebANNSTLF 6.0). The new version, which retains almost all the functionally of ANNSTLF 5.1, features a web-based user interface that makes it possible to exploit a wide range of web services.

2007-09-17T23:59:59.000Z

450

Optimization of an Oil Production System using Neural Networks and Genetic Algorithms Guillermo Jimenez de la C 1,2  

E-Print Network (OSTI)

Abstract — This paper proposes an optimization strategy which is based on neural networks and genetic algorithms to calculate the optimal values of gas injection rate and oil rate for oil production system. Two cases are analyzed: a) A single well production system and b) A production system composed by two gaslifted wells. For both cases an objective function is maximized to reduce production cost. The proposed strategy shows the ability of the neural networks to approximate the behavior of an oil production system and the genetic algorithms to solve optimization problems when a mathematical model is not available.

Jose A. Ruz-hernandez; Ruben Salazar M; Evgen Shelomov

2009-01-01T23:59:59.000Z

451

Application of Paraconsistent Artificial Neural Networks as a Method of Aid in the Diagnosis of Alzheimer Disease  

Science Conference Proceedings (OSTI)

The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient ... Keywords: Alzheimer disease, Artificial neural network, Electroencephalogram, Paraconsistent logic, Pattern recognition

Helder Frederico Silva Lopes; Jair M. Abe; Renato Anghinah

2010-12-01T23:59:59.000Z

452

Identification of Boiling Two-phase Flow Patterns in Water Wall Tube Based on BP Neural Network  

Science Conference Proceedings (OSTI)

In this paper, the boiling phenomena of steam boiler under atmospheric pressure are simulated by using the UDF program of CFD software. Characteristics including pressure, temperature and vapor fraction respectively for bubble, slug and annular flow ... Keywords: Boiling heat transfer, BP neural network, flow pattern, coefficient of heat transfer

Lei Guo; Shusheng Zhang; Yaqun Chen; Lin Cheng

2010-06-01T23:59:59.000Z

453

Dynamic filter weights neural network model integrated with differential evolution for day-ahead price forecasting in energy market  

Science Conference Proceedings (OSTI)

In this paper a new dynamic model for forecasting electricity prices from 1 to 24h in advance is proposed. The model is a dynamic filter weight Adaline using a sliding mode weight adaptation technique. The filter weights for this neuron constitute of ... Keywords: Differential evolution, Dynamic filter weights neuron, Energy market, Local linear wavelet neural network, Sliding mode control

S. Chakravarty; P. K. Dash

2011-09-01T23:59:59.000Z

454

Predicting the spatial distribution of soil profile in Adapazari/Turkey by artificial neural networks using CPT data  

Science Conference Proceedings (OSTI)

The infamous soils of Adapazari, Turkey, that failed extensively during the 46-s long magnitude 7.4 earthquake in 1999 have since been the subject of a research program. Boreholes, piezocone soundings and voluminous laboratory testing have enabled researchers ... Keywords: Artificial neural networks, Cone penetration test, Site characterization, Soil classification, Soil profile, Spatial distribution.

Ersin Arel

2012-06-01T23:59:59.000Z

455

Prediction of Aortic Diameter Values in Healthy Turkish Infants, Children, and Adolescents by Using Artificial Neural Network  

Science Conference Proceedings (OSTI)

The aorta is the largest vessel in the systemic circuit. Its diameter is very important to guess for child before adult age, due to growing up body. Aortic diameter, one of the cardiac values, changes in time. Evaluation of the cardiac structures and ... Keywords: Aortic diameter, Artificial neural network, Echocardiography, Line based, Normalization, Pediatric cardiology

Bayram Akdemir; Bülent Oran; Salih Gunes; Sevim Karaaslan

2009-10-01T23:59:59.000Z

456

A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction  

Science Conference Proceedings (OSTI)

Precise prediction of stock prices is difficult chiefly because of the many intervening factors. Unpredictability is particularly notable in the aftermath of the global financial crisis. Data mining may however be used to discover highly correlated estimation ... Keywords: Artificial neural networks (ANN), C&RT, Decision tree, Stock price forecasting

Tsung-Sheng Chang

2011-11-01T23:59:59.000Z

457

Integrated use of artificial neural networks and genetic algorithms for problems of alarm processing and fault diagnosis in power systems  

Science Conference Proceedings (OSTI)

This work approaches relative aspects to the alarm processing problem and fault diagnosis in system level, having as purpose filter the alarms generated during a outage and identify the equipment under fault. A methodology was developed using Artificial ... Keywords: alarm processing, fault diagnosis, genetic algorithms, neural network, supervision and control of electrical systems

Paulo Cícero Fritzen; Ghendy Cardoso, Jr.; João Montagner Zauk; Adriano Peres De Morais; Ubiratan H. Bezerra; Joaquim A. P. M. Beck

2010-03-01T23:59:59.000Z

458

A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides  

Science Conference Proceedings (OSTI)

Tertiary Protein Structure Prediction is one of the most important problems in Structural Bioinformatics. Along the last 20years many algorithms have been proposed as to solve this problem. However, it still remains a challenging issue because of the ... Keywords: Artificial Neural Networks, Group Method of Data Handling, Multilayer Perceptron, Protein Structure Prediction

MáRcio Dorn; André L. S. Braga; Carlos H. Llanos; Leandro S. Coelho

2012-11-01T23:59:59.000Z

459

Short-term Wind Power Prediction for Offshore Wind Farms -Evaluation of Fuzzy-Neural Network Based Models  

E-Print Network (OSTI)

Short-term Wind Power Prediction for Offshore Wind Farms - Evaluation of Fuzzy-Neural Network Based of wind power capacities are likely to take place offshore. As for onshore wind parks, short-term wind of offshore farms and their secure integration to the grid. Modeling the behavior of large wind farms

Paris-Sud XI, Université de

460

A new probabilistic approach to independent component analysis suitable for on-line learning in artificial neural networks  

Science Conference Proceedings (OSTI)

Recently, elements of probabilistic model that are suitable for modeling of learning algorithms in biologically plausible artificial neural networks framework, have been introduced. Model was based on two of the main concepts in quantum physics --- a ... Keywords: born rule, local learning rules, probabilistic independent component analysis, tsallis entropy

Marko V. Jankovic; Neil Rubens

2012-11-01T23:59:59.000Z

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.


461

Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks  

Science Conference Proceedings (OSTI)

Effluent ammonia-nitrogen (NH3-N), chemical oxygen demand (COD) and total nitrogen (TN) removals are the most common environmental and process performance indicator for all types of wastewater treatment plants (WWTPs). In this paper, a soft computing ... Keywords: neural network, fuzzy rough sets, input variable selection, wastewater treatment, prediction, soft computing

Fei Luo; Ren-hui Yu; Yu-ge Xu; Yan Li

2009-08-01T23:59:59.000Z

462

The Quality Monitoring Technology in the Process of the Pulping Papermaking Alkaline Steam Boiling Based on Neural Network  

Science Conference Proceedings (OSTI)

On the status quo that being lack of the testing equipment which gives reliable and direct parameters on measuring the quality of pulp in the cooking process, this article focus on the lignin value soft-measurement technology in the pulp and papermaking ... Keywords: Neural network, Pulp and papermaking, Soft-measurement model

Jianjun Su; Yanmei Meng; Chaolin Chen; Funing Lu; Sijie Yan

2008-09-01T23:59:59.000Z

463

Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator  

Science Conference Proceedings (OSTI)

In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography ... Keywords: Cognitive load, Heart rate analysis, Intelligent systems, Nonlinear data analysis, Psychophysiological stress factors

Manne Hannula; Kerttu Huttunen; Jukka Koskelo; Tomi Laitinen; Tuomo Leino

2008-11-01T23:59:59.000Z

464

A novel defect classification system of cast-resin transformers by neural network under acoustic emission signal  

Science Conference Proceedings (OSTI)

Degraded insulating property of electric equipments will lead to serious accident and great loss for the utilities and customers. Partial discharge detection is an efficient diagnosis method to prevent the failure of electric equipments arising from ... Keywords: acoustic emission, neural network, partial discharge, transformer

Cheng-Chien Kuo; Teng-Fa Tsao

2007-04-01T23:59:59.000Z

465

Bowel-sound pattern analysis using wavelets and neural networks with application to long-term, unsupervised, gastrointestinal motility monitoring  

Science Conference Proceedings (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

466

Determining the angles of break of the mining subsidence basin by the neural network with genetic algorithm  

Science Conference Proceedings (OSTI)

The angle of break is a key factor that determines the mining damage extent of the surface in a mine, and it is also used to depict the characteristics of the mining subsidence basin. The geological and mining factors that influence the angle of break ... Keywords: angle of break, genetic algorithm, neural network, subsidence basin

Hua-bin Chai

2010-03-01T23:59:59.000Z

467

MP-Draughts: a multiagent reinforcement learning system based on MLP and Kohonen-SOM neural networks  

Science Conference Proceedings (OSTI)

This paper presents MP-Draughts (MultiPhase-Draughts): a multiagent environment for Draughts, where one agent - named IIGA- is built and trained such as to be specialized for the initial and the intermediate phases of the games and the remaining ones ... Keywords: Kohonen self-organizing maps, clustering, draughts, games, machine learning, neural networks, reinforcement learning, temporal difference

Valquiria Aparecida Rosa Duarte; Rita Maria Silva Julia; Ayres Roberto Araujo Barcelos; Alana Bueno Otsuka

2009-10-01T23:59:59.000Z

468

Adaptive control using neural network for command following of tilt-rotor airplane in 0°-tilt angle mode  

Science Conference Proceedings (OSTI)

This paper deals with an autonomous flight algorithm design problem for the tilt-rotor airplane under development by Korea Aerospace Research Institute for simulation study. The objective of this paper is to design a guidance and control algorithm to ... Keywords: KARI tilt-rotor airplane, adaptive control, approximate modelbased inversion, command following, neural network

Jae Hyoung Im; Cheolkeun Ha

2009-09-01T23:59:59.000Z

469

HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL FOR FORECASTING TIME-SERIES DATA  

Science Conference Proceedings (OSTI)

The aim of this study is to develop a new hybrid model by combining a linear and nonlinear model for forecasting time-series data. The proposed model (GRANN_ARIMA) integrates nonlinear grey relational artificial neural network (GRANN) and a linear autoregressive ...

Roselina Sallehuddin; Siti Mariyam Hj. Shamsuddin

2009-05-01T23:59:59.000Z

470

Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs)  

Science Conference Proceedings (OSTI)

Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial ... Keywords: Artificial Neural Networks (ANNs), Auto-Regressive Integrated Moving Average (ARIMA), Exchange rate, Financial markets, Fuzzy logic, Time series forecasting

Mehdi Khashei; Mehdi Bijari; Gholam Ali Raissi Ardali

2009-01-01T23:59:59.000Z

471

Detection and classification of volatile organic compounds using Indium Tin Oxide sensor array and artificial neural network  

Science Conference Proceedings (OSTI)

This article reveals the novel approach of fabricating Indium Tin Oxide thin films grown on glass substrate at 648 K temperatures using direct evaporation method for detection of small concentration volatile organic compounds (VOCs) and their ... Keywords: ANNs, ITO thin films, VOC mixtures, VOCs, artificial neural networks, direct evaporation, indium tin oxide, sensor arrays, thin film sensors, volatile organic compounds

H. J. Pandya

2009-05-01T23:59:59.000Z

472

Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives  

Science Conference Proceedings (OSTI)

Performance optimization of electrical drives implies a lot of degrees of freedom in the variation of design parameters, which in turn makes the process overly complex and sometimes impossible to handle for classical analytical optimization approaches. ... Keywords: Electrical drives, Feed-forward artificial neural networks, Hybridization, Multi-objective evolutionary algorithms, Performance optimization, Surrogate fitness evaluation

Alexandru-Ciprian Zvoianu, Gerd Bramerdorfer, Edwin Lughofer, Siegfried Silber, Wolfgang Amrhein, Erich Peter Klement

2013-09-01T23:59:59.000Z

473

Nonlinear model identification and adaptive control of CO2 sequestration process in saline aquifers using artificial neural networks  

Science Conference Proceedings (OSTI)

In recent years, storage of carbon dioxide (CO"2) in saline aquifers has gained intensive research interest. The implementation, however, requires further research studies to ensure it is safe and secure operation. The primary objective is to secure ... Keywords: Carbon dioxide sequestration, Extended Kalman filter (EKF), GAP-RBF neural network, Nonlinear model predictive control (NMPC), System identification, Unscented Kalman filter (UKF)

Karim Salahshoor; Mohammad Hasan Hajisalehi; Morteza Haghighat Sefat

2012-11-01T23:59:59.000Z

474

Applying neural networks, genetic algorithms and fuzzy logic for the identification of cracks in shafts by using coupled response measurements  

Science Conference Proceedings (OSTI)

This paper considers the dynamic behavior of a shaft with two transverse cracks characterized by three measures: position, depth and relative angle. Both cracks are considered to lie along arbitrary angular positions with respect to the longitudinal ... Keywords: Coupling, Crack identification, Genetic algorithms, Neural networks, Shaft

K. M. Saridakis; A. C. Chasalevris; C. A. Papadopoulos; A. J. Dentsoras

2008-06-01T23:59:59.000Z

475

BGNN Neural Network Based on Improved E.Coli Foraging Optimization Algorithm Used in the Nonlinear Modeling of Hydraulic Turbine  

Science Conference Proceedings (OSTI)

A novel Bayesian-Gaussian neural network (BGNN) is proposed in this paper for the nonlinear modeling of hydraulic turbine which is difficult to obtain its mathematical model because of its complex and nonlinear characteristics. The topology and connection ... Keywords: BGNN, Hydraulic turbine, Improved E.Coli foraging optimization algorithm, Nonlinear modeling

Yijian Liu; Yanjun Fang

2009-05-01T23:59:59.000Z

476

Short-Term PV Generation System Direct Power Prediction Model on Wavelet Neural Network and Weather Type Clustering  

Science Conference Proceedings (OSTI)

With the increase of the capacity of PV generated systems, how to eliminate the problem caused by the randomness of power output for photovoltaic system becomes more significant. Most of the existing photovoltaic prediction is Based on the solar radiation. ... Keywords: PV generation system, Wavelet neural network, Weather type clustering, Direct prediction

Ying Yang, Lei Dong

2013-08-01T23:59:59.000Z

477

RATE COEFFICIENTS FOR THE COLLISIONAL EXCITATION OF MOLECULES: ESTIMATES FROM AN ARTIFICIAL NEURAL NETWORK  

Science Conference Proceedings (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

478

Predicting Geomagnetic Storms From Solar-Wind Data Using Time-Delay Neural Networks  

E-Print Network (OSTI)

. We have used time-delay feed-forward neural networks to compute the geomagnetic activity index D st one hour ahead from a temporal sequence of solar wind data. The input data includes solar-wind density n, velocity V and the southward component B z of the interplanetary magnetic field. D st is not included in the input data. The networks implement an explicit functional relationship between the solar wind and the geomagnetic disturbance, including both direct and time-delayed nonlinear relations. In this study we specially consider the influence of varying the temporal size of the input data sequence. The networks are trained on data covering 6600 h, and tested on data covering 2100 h. It is found that the initial and main phases of geomagnetic storms are well predicted, almost independent of the length of the inputdata sequence. However, to predict the recovery phase, we have to use up to 20 h of solar-wind input data. The recovery phase is mainly governed by the ring-current loss...

Gleisner Lundstedt; H. Gleisner; H. Lundstedt; P. Wintoft

1996-01-01T23:59:59.000Z

479

Applying Bayesian Neural Networks to Separate Neutrino Events from Backgrounds in Reactor Neutrino Experiments  

E-Print Network (OSTI)

A toy detector has been designed to simulate central detectors in reactor neutrino experiments in the paper. The samples of neutrino events and three major backgrounds from the Monte-Carlo simulation of the toy detector are generated in the signal region. The Bayesian Neural Networks(BNN) are applied to separate neutrino events from backgrounds in reactor neutrino experiments. As a result, the most neutrino events and uncorrelated background events in the signal region can be identified with BNN, and the part events each of the fast neutron and $^{8}$He/$^{9}$Li backgrounds in the signal region can be identified with BNN. Then, the signal to noise ratio in the signal region is enhanced with BNN. The neutrino discrimination increases with the increase of the neutrino rate in the training sample. However, the background discriminations decrease with the decrease of the background rate in the training sample.

Ye Xu; Yixiong Meng; Weiwei Xu

2008-08-02T23:59:59.000Z

480

A Neural Network Model of Metric Perception and Cognition in the Audition of Functional Tonal Music.  

E-Print Network (OSTI)

In our previous work we proposed a theory of cognition of tonal music based on control of expectations and created a model to test the theory using a hierarchical sequential neural network. The net learns metered and rhythmecized functional tonal harmonic progressions allowing us to measure fluctuations in the degree of realized expectation (DRE). Preliminary results demonstrated the necessity of including metric information in the model in order to obtain more realistic results for the model of the DRE. This was achieved by adding two units representing periodic index of meter to the input layer. In this paper we describe significant extensions to the architecture. Specifically, our goal was to represent more general meter tracking strategies and consider their implications as cognitive models. The output layer of the sub-net for metric information is fully connected to the hidden layer of sequential net. This output layer includes pools of three and four units representing duple and ...

Jonathan Berger

1997-01-01T23:59:59.000Z

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.


481

Estimation of soil moisture in paddy field using Artificial Neural Networks  

E-Print Network (OSTI)

In paddy field, monitoring soil moisture is required for irrigation scheduling and water resource allocation, management and planning. The current study proposes an Artificial Neural Networks (ANN) model to estimate soil moisture in paddy field with limited meteorological data. Dynamic of ANN model was adopted to estimate soil moisture with the inputs of reference evapotranspiration (ETo) and precipitation. ETo was firstly estimated using the maximum, average and minimum values of air temperature as the inputs of model. The models were performed under different weather conditions between the two paddy cultivation periods. Training process of model was carried out using the observation data in the first period, while validation process was conducted based on the observation data in the second period. Dynamic of ANN model estimated soil moisture with R2 values of 0.80 and 0.73 for training and validation processes, respectively, indicated that tight linear correlations between observed and estimated values of s...

Arif, Chusnul; Setiawan, Budi Indra; Doi, Ryoichi

2013-01-01T23:59:59.000Z

482

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 is proposed based on the RBF neural network with the associated parameters of sample deviation and partial sample deviation, which are defined for the purpose of effective judgment of new samples. Also, in order to forecast the load of sample with large deviation, sensitivity coefficients of input layer is given in this paper. To validate this model, an experiment is performed on a thermoelectric plant, and the experimental result indicates that the network can be put into extensive use for short-term load forecasting of thermoelectric utility.

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

2006-01-01T23:59:59.000Z

483

Discrimination Analysis of Earthquakes and Man-Made Events Using ARMA Coefficients Determination by Artificial Neural Networks  

Science Conference Proceedings (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

484

Feasibility of calculating petrophysical properties in tight-sand reservoirs using neural networks. Final report, October 1989-July 1991  

Science Conference Proceedings (OSTI)

The objective of the research was to determine the feasibility of using neural networks to estimate petrophysical properties in tight sand reservoirs. A second objective was to gain some experience concerning how to approach the development of a future prototype, including what should be done and what should be avoided. Gas Research Institute (GRI) focused the project on tight sands because they contain enormous gas reserves and their complicated lithology represents a challenge to log analysts. The data were supplied by GRI from two of its geographically proximate experimental wells in tight sand formations. The nets were tested in sections of those wells that were not used for training, and in two other wells, one in a geographically close but geologically unrelated formation and one in Wyoming. The feasibility testing demonstrated that the relatively simple neural networks developed have comparable accuracy with standard logging analysis estimates in wells that contributed data to the training set. Transportability of the network was tested by using core measurements in two wells in which the nets were not trained, with inconclusive results. Recommendations were made to increase the accuracy of the neural networks.

Urquidi-Macdonald, M.; Javitz, H.S.; Park, W.; Lee, J.D.; Bergman, A.

1991-07-01T23:59:59.000Z

485

Wavelet Analysis of Seasonal Rainfall Variability of the Upper Blue Nile Basin, its Teleconnection to Global Sea Surface Temperature, and its Forecasting by an Artificial Neural Network  

Science Conference Proceedings (OSTI)

Rainfall is the primary driver of basin hydrologic processes. This article developed a rainfall predictive tool that combines the wavelet principal component analysis (WPCA), artificial neural networks–genetic algorithm (ANN–GA), statistical ...

Mohamed Helmy Elsanabary; Thian Yew Gan

486

Accurate and Fast Neural Network Emulations of Model Radiation for the NCEP Coupled Climate Forecast System: Climate Simulations and Seasonal Predictions  

Science Conference Proceedings (OSTI)

The approach to accurate and fast-calculating model physics using neural network emulations was previously developed by the authors for both longwave and shortwave radiation parameterizations or the full model radiation, which is the most time-...

V. M. Krasnopolsky; M. S. Fox-Rabinovitz; Y. T. Hou; S. J. Lord; A. A. Belochitski

2010-05-01T23:59:59.000Z

487

Technical analysis : neural network based pattern recognition of technical trading indicators, statistical evaluation of their predictive value and a historical overview of the field  

E-Print Network (OSTI)

We revisit the kernel regression based pattern recognition algorithm designed by Lo, Mamaysky, and Wang (2000) to extract nonlinear patterns from the noisy price data, and develop an analogous neural network based one. We ...

Hasanhodzic, Jasmina, 1979-

2004-01-01T23:59:59.000Z

488

New Approach to Calculation of Atmospheric Model Physics: Accurate and Fast Neural Network Emulation of Longwave Radiation in a Climate Model  

Science Conference Proceedings (OSTI)

A new approach based on a synergetic combination of statistical/machine learning and deterministic modeling within atmospheric models is presented. The approach uses neural networks as a statistical or machine learning technique for an accurate ...

Vladimir M. Krasnopolsky; Michael S. Fox-Rabinovitz; Dmitry V. Chalikov

2005-05-01T23:59:59.000Z

489

Temperature decoupling control of double-level air flow field dynamic vacuum system based on neural network and prediction principle  

Science Conference Proceedings (OSTI)

Double-level air flow field dynamic vacuum (DAFDV) system is a strong coupling, large time-delay, and nonlinear multi-input-multi-output system. Decoupling and overcoming the impact of time-delay are two keys to obtain rapid, accurate and independent ... Keywords: ASSAVP, BP, DAFDV, Decoupling control, Double-level air flow field, EBTC, HX, IPSO, MIMO, Neural networks, OIF, PID, Particle swarm optimization, Prediction, RBF, SISO, TITO, WBTC

Li Jinyang; Meng Xiaofeng

2013-04-01T23:59:59.000Z

490

Applying least squares support vector machines to the airframe wing-box structural design cost estimation  

Science Conference Proceedings (OSTI)

This research used the least squares support vector machines (LS-SVM) method to estimate the project design cost of an airframe wing-box structure. We also compared the estimation performance using back-propagation neural networks (BPN) and statistical ... Keywords: Airframe structure, Back-propagation neural networks, Cost estimation, Least squares support vector machines, Response surface methodology

S. Deng; Tsung-Han Yeh

2010-12-01T23:59:59.000Z

491

Search for Standard Model Higgs Boson Production in Association with a W Boson using a Neural Network  

SciTech Connect

We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions (p{bar p} {yields} W{sup {+-}}H {yields} {ell}{nu}b{bar b}) at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9 fb{sup -1}. We select events consistent with a signature of a single charged lepton (e{sup {+-}}/{mu}{sup {+-}}), missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to 150 GeV/c{sup 2}, respectively.

Aaltonen, T.; /Helsinki Inst. of Phys.; Adelman, Jahred A.; /Chicago U., EFI; Akimoto, T.; /Tsukuba U.; Alvarez Gonzalez, B.; /Cantabria Inst. of Phys.; Amerio, S.; /INFN, Padua; Amidei, Dante E.; /Michigan U.; Anastassov, A.; /Northwestern U.; Annovi, Alberto; /Frascati; Antos, Jaroslav; /Comenius U.; Apollinari, G.; /Fermilab; Apresyan, A.; /Purdue U. /Waseda U.

2009-05-01T23:59:59.000Z

492

Forecasting of preprocessed daily solar radiation time series using neural networks  

SciTech Connect

In this paper, we present an application of Artificial Neural Networks (ANNs) in the renewable energy domain. We particularly look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. We have used a MLP and an ad hoc time series pre-processing to develop a methodology for the daily prediction of global solar radiation on a horizontal surface. First results are promising with nRMSE {proportional_to} 21% and RMSE {proportional_to} 3.59 MJ/m{sup 2}. The optimized MLP presents predictions similar to or even better than conventional and reference methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors. Moreover we found that the data pre-processing approach proposed can reduce significantly forecasting errors of about 6% compared to conventional prediction methods such as Markov chains or Bayesian inference. The simulator proposed has been obtained using 19 years of available data from the meteorological station of Ajaccio (Corsica Island, France, 41 55'N, 8 44'E, 4 m above mean sea level). The predicted whole methodology has been validated on a 1.175 kWc mono-Si PV power grid. Six prediction methods (ANN, clear sky model, combination..) allow to predict the best daily DC PV power production at horizon d + 1. The cumulated DC PV energy on a 6-months period shows a great agreement between simulated and measured data (R{sup 2} > 0.99 and nRMSE < 2%). (author)

Paoli, Christophe; Muselli, Marc; Nivet, Marie-Laure [University of Corsica, CNRS UMR SPE, Corte (France); Voyant, Cyril [University of Corsica, CNRS UMR SPE, Corte (France); Hospital of Castelluccio, Radiotherapy Unit, Ajaccio (France)

2010-12-15T23:59:59.000Z

493

Neural networks and separation of background and foregrounds in astrophysical sky maps  

E-Print Network (OSTI)

The Independent Component Analysis (ICA) algorithm is implemented as a neural network for separating signals of different origin in astrophysical sky maps. Due to its self-organizing capability, it works without prior assumptions on the signals, neither on their frequency scaling, nor on the signal maps themselves; instead, it learns directly from the input data how to separate the physical components, making use of their statistical independence. To have a first insight into the capabilities of this approach, we apply the ICA algorithm on sky patches, taken from simulations and observations, at the microwave frequencies, that are going to be deeply explored in a few years on the whole sky, by the Microwave Anisotropy Probe (MAP) and by the Planck Surveyor Satellite. The maps are at the frequencies of the Low Frequency Instrument (LFI) aboard the Planck satellite (30, 44, 70 and 100 GHz), and contain simulated astrophysical radio sources, Cosmic Microwave Background (CMB) radiation, and Galactic diffuse emissions from thermal dust and synchrotron. We show that the ICA algorithm is able to recover each

C. Baccigalupi; L. Bedini; C. Burigana; G. De Zotti; A. Farusi; D. Maino; M. Maris; F. Perrotta; E. Salerno; L. Toffolatti; A. Tonazzini

2008-01-01T23:59:59.000Z

494

Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices  

E-Print Network (OSTI)

This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices for 1, 2, 3,and four months to maturity, one by one and also altogether. The results on the benchmark suggest that a dynamic model of 13 lags is the optimal to forecast spot price direction for the short-term. Further, the forecast accuracy of the direction of the market was 78%, 66%, and 53% for one, two, and three days in future conclusively. For all the experiments, that include futures data as an input, the results show that on the short-term, futures prices do hold new information on the spot price direction. The results obtained will generate comprehensive understanding of the cr...

Kulkarni, Siddhivinayak

2009-01-01T23:59:59.000Z

495

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 was funded in part by a grant from the U.S. Environmental Protection Agency (EPA), Region VI. Combustion control is quickly becoming an emerging alternative for reducing utility plant emissions without installing costly "end of pipe" controls. The LCRA estimates that the technology has the potential to improve the thermal efficiency of a large utility boiler by more than 1 percent. Preliminary pilot test results indicate that a 0.5 percent improvement in thermal efficiency at the 430 MW gas-fired utility boiler will result in an estimated energy savings of 76,000 mmBtus and carbon dioxide (CO2) reductions of 4,079 tons per year. This paper describes the processes that were undertaken to identify and implement the pilot project at LCRA's Thomas C. Ferguson Power Plant, located in Marble Falls, Texas. Activities performed and documented include lessons learned, equipment selection, data acquisition, model evaluation and projected emission reductions.

Johnson, M. L.

1998-04-01T23:59:59.000Z

496

Combined expert system/neural networks method for process fault diagnosis  

DOE Patents (OSTI)

A two-level hierarchical approach for process fault diagnosis of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.

Reifman, J.; Wei, T.Y.C.

1995-08-15T23:59:59.000Z

497

Combined expert system/neural networks method for process fault diagnosis  

DOE Patents (OSTI)

A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.

Reifman, Jaques (Westchester, IL); Wei, Thomas Y. C. (Downers Grove, IL)

1995-01-01T23:59:59.000Z

498

Fault diagnosis for the feedwater heater system of a 300MW coal-fired power generating unit based on RBF neural network  

Science Conference Proceedings (OSTI)

In this paper, a new style radial basis function (RBF) neural network is used for fault diagnosis of the high-pressure feed-water heater system of a coal-fired power generating unit. The structure of the RBF network and its training algorithm are given. ...

Liangyu Ma; Yongguang Ma; Jin Ma

2005-08-01T23:59:59.000Z

499

A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings  

Science Conference Proceedings (OSTI)

This paper addresses the problem of predicting demand for natural gas for the purpose of realizing energy cost savings. Daily monitoring of a rooftop unit wireless sensor system provided feedback for a decision support system that supplied the demand ... Keywords: Artificial neural networks, Decision support system, Energy forecasting, Natural gas demand, Nearest neighbor method, Wireless sensor networks

James A. Rodger

2014-03-01T23:59:59.000Z

500

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 38, NO. 3, MAY/JUNE 2002 879 Neural-Network-Based Model Reference Adaptive  

E-Print Network (OSTI)

an NN in the adaptation mechanism. The technique is applied to a permanent-magnet syn- chronous motor, neural networks, online parameter adaptation, permanent-magnet synchronous motor. I. INTRODUCTION ANUMBER-Network-Based Model Reference Adaptive Systems for High-Performance Motor Drives and Motion Controls Malik E. Elbuluk

Husain, Iqbal