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1

Computationally Efficient Neural Network Intrusion Security Awareness  

SciTech Connect (OSTI)

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

2

Seismic active control by neural networks.  

SciTech Connect (OSTI)

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

Tang, Y.

1998-01-01T23:59:59.000Z

3

Facial Expression Classification Using RBF AND Back-Propagation Neural Networks R.Q.Feitosa1,2  

E-Print Network [OSTI]

of Computer Engineering e-mail: [raul, marley]@ele.puc -rio.br, tuler@inf.puc-rio.br, [diogo, sam. The classification system is based on attributes extracted from human faces images using the principal component analysis (PCA) technique. Well-framed images were used in order to simplify the face detection on the image

4

Automatic Classi cation of Subdwarf Spectra using a Neural Network C. Winter 1 , C.S. Je ery 1 and J.S. Drilling 2  

E-Print Network [OSTI]

1 and J.S. Drilling 2 1 Armagh Observatory, College Hill, Armagh BT61 9DG, N. Ireland 2 Dept@arm.ac.uk, drilling@rouge.phys.lsu.edu Abstract We apply a multilayer feed-forward back propagation arti#12;cial neural network to a sample of 380 subdwarf spectra classi#12;ed by Drilling et al. (2002), showing

Jeffery, Simon

5

Using Neural Networks  

E-Print Network [OSTI]

unmeasurable parameters in a first-principles mathematical model of the engine. The network is trained using data derived from measured data taken on an auxiliary power unit (APU) engine (from an aircraft application). A discussion of the neural network...

Gabel, S.

6

Development of Fast-Running Simulation Methodology Using Neural Networks for Load Follow Operation  

SciTech Connect (OSTI)

A new fast-running analytic model has been developed for analyzing the load follow operation. The new model was based on the neural network theory, which has the capability of modeling the input/output relationships of a nonlinear system. The new model is made up of two error back-propagation neural networks and procedures to calculate core parameters, such as the distributions and density of xenon in a quasi-steady-state core like load follow operation. One neural network is designed to retrieve the axial offset of power distribution, and the other is for reactivity corresponding to a given core condition. The training data sets for learning the neural networks in the new model are generated with a three-dimensional nodal code and, also, the measured data of the first-day test of load follow operation. Using the new model, the simulation results of the 5-day load follow test in a pressurized water reactor show a good agreement between the simulation data and the actual measured data. Required computing time for simulating a load follow operation is comparable to that of a fast-running lumped model. Moreover, the new model does not require additional engineering factors to compensate for the difference between the actual measurements and analysis results because the neural network has the inherent learning capability of neural networks to new situations.

Seong, Seung-Hwan [Korea Atomic Energy Research Institute (Korea, Republic of); Park, Heui-Youn [Korea Atomic Energy Research Institute (Korea, Republic of); Kim, Dong-Hoon [Korea Atomic Energy Research Institute (Korea, Republic of); Suh, Yong-Suk [Korea Atomic Energy Research Institute (Korea, Republic of); Hur, Seop [Korea Atomic Energy Research Institute (Korea, Republic of); Koo, In-Soo [Korea Atomic Energy Research Institute (Korea, Republic of); Lee, Un-Chul [Seoul National University (Korea, Republic of); Jang, Jin-Wook [Seoul National University (Korea, Republic of); Shin, Yong-Chul [Yonsei University (Korea, Republic of)

2002-05-15T23:59:59.000Z

7

attractor neural network: Topics by E-print Network  

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

Convolutional Neural Networks 5 Mixture of density networks Englebienne, Gwenn 138 Fuzzy neural network pattern recognition algorithm for classification of the events in...

8

analog neural network: Topics by E-print Network  

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

Convolutional Neural Networks 5 Mixture of density networks Englebienne, Gwenn 118 Fuzzy neural network pattern recognition algorithm for classification of the events in...

9

attractor neural networks: Topics by E-print Network  

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

Convolutional Neural Networks 5 Mixture of density networks Englebienne, Gwenn 138 Fuzzy neural network pattern recognition algorithm for classification of the events in...

10

artifical neural network: Topics by E-print Network  

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

8, Nos. 1-2 Cytological Diagnosis Based on Fuzzy Neural Networks max combined with a fuzzy neural network approach, for the discrimination of benign from malignant gastric...

11

artifical neural networks: Topics by E-print Network  

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

8, Nos. 1-2 Cytological Diagnosis Based on Fuzzy Neural Networks max combined with a fuzzy neural network approach, for the discrimination of benign from malignant gastric...

12

artificial neural network: Topics by E-print Network  

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

neural networks, for phosphene localisation are used Rattray, Magnus 63 Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks Computer Technologies...

13

artificial neural networks: Topics by E-print Network  

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

neural networks, for phosphene localisation are used Rattray, Magnus 63 Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks Computer Technologies...

14

Automatic Generation of Initial Weights and Estimation of Hidden Units for Pattern Classifcation Using Neural Networks  

E-Print Network [OSTI]

This study high lights on the subject of weight initialization in back-propagation feed-forward networks. Training data is analyzed and the notion of critical points is introduced for determining the initial weights and ...

Keeni, Kanad; Nakayama, Kenji; Shimodaira, Hiroshi

15

Advanced battery modeling using neural networks  

E-Print Network [OSTI]

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

Arikara, Muralidharan Pushpakam

1993-01-01T23:59:59.000Z

16

Aircraft System Identification Using Artificial Neural Networks  

E-Print Network [OSTI]

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

Valasek, John

17

Neural Networks Perceptrons and Backpropagation  

E-Print Network [OSTI]

neural network Inputs x = (x1, x2) = (a1, a2) a5 = g(W3,5a3 + W4,5a4) a5 = g(W3,5g(W1,3a1 + W2,3a2) + W4,5g(W1,4a1 + W2,4a2)) function hW(x) is computed Neural Networks 8 / 17 #12;Network structure Network network Inputs x = (x1, x2) = (a1, a2) a5 = g(W3,5a3 + W4,5a4) a5 = g(W3,5g(W1,3a1 + W2,3a2) + W4,5g(W1,4a

Bremen, Universität

18

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

SciTech Connect (OSTI)

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

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

2013-02-15T23:59:59.000Z

19

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

SciTech Connect (OSTI)

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

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

2011-01-17T23:59:59.000Z

20

Foundations of Artificial Intelligence Neural Networks  

E-Print Network [OSTI]

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

Qu, Rong

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

Oil reservoir properties estimation using neural networks  

SciTech Connect (OSTI)

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

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

1997-02-01T23:59:59.000Z

22

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

23

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

24

adult murine neural: Topics by E-print Network  

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

of solutions 3. Recently, a new artificial neural network algorithm Serpen, Gursel 495 Fuzzy neural network pattern recognition algorithm for classification of the events in...

25

antidepressants increase neural: Topics by E-print Network  

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

of solutions 3. Recently, a new artificial neural network algorithm Serpen, Gursel 496 Fuzzy neural network pattern recognition algorithm for classification of the events in...

26

Paraphrastic Neural Network Language Models  

E-Print Network [OSTI]

PARAPHRASTIC NEURAL NETWORK LANGUAGE MODELS X. Liu, M. J. F. Gales & P. C. Woodland Cambridge University Engineering Dept, Trumpington St., Cambridge, CB2 1PZ U.K. Email: {xl207,mjfg,pcw}@eng.cam.ac.uk ABSTRACT Expressive richness in natural... Workshop on Artificial Intelligence and Statistics, Barbados, 2005, pp.246-252. [24] M. Mohri (1997). ďFinite-state transducers in language and speech processingĒ, Computational Linguistics, 23:2, 1997. [25] J. Park, X. Liu, M. J. F. Gales and P. C...

Liu, X.; Gales, M. J. F.; Woodland, P. C.

2014-01-01T23:59:59.000Z

27

Fundamental building blocks for a compact optoelectronic neural network processor  

E-Print Network [OSTI]

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

Ruedlinger, Benjamin Franklin, 1976-

2003-01-01T23:59:59.000Z

28

Enhancing neural-network performance via assortativity  

SciTech Connect (OSTI)

The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations - assortativity - on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computationally, which is independent of how the topology may have evolved. We show how the robustness to noise is greatly enhanced in assortative (positively correlated) neural networks, especially if it is the hub neurons that store the information.

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

2011-03-15T23:59:59.000Z

29

Nonlinear programming with feedforward neural networks.  

SciTech Connect (OSTI)

We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.

Reifman, J.

1999-06-02T23:59:59.000Z

30

applying neural networks: Topics by E-print Network  

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

discrimination of benign from malignant gastric neural network classifier, an efficient pattern recognition approach, is used to classify benign Likas, Aristidis 48 A conjugate...

31

adaptive neural networks: Topics by E-print Network  

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

their spatial scale. Applications of the new techn... Jonathan A. Marshall 1995-01-01 46 Fuzzy neural network pattern recognition algorithm for classification of the events in...

32

applying neural network: Topics by E-print Network  

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

discrimination of benign from malignant gastric neural network classifier, an efficient pattern recognition approach, is used to classify benign Likas, Aristidis 48 A conjugate...

33

automata neural networks: Topics by E-print Network  

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

and genetic programming. In this line of research, we have also Cho, Sung-Bae 115 Fuzzy neural network pattern recognition algorithm for classification of the events in...

34

adaptive neural network: Topics by E-print Network  

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

their spatial scale. Applications of the new techn... Jonathan A. Marshall 1995-01-01 46 Fuzzy neural network pattern recognition algorithm for classification of the events in...

35

Methods to Speed Up Error Back-Propagation Learning Algorithm DILIP SARKAR  

E-Print Network [OSTI]

Methods to Speed Up Error Back-Propagation Learning Algorithm DILIP SARKAR University of Miami's address: D. Sarkar, Department of Mathematics and Computer Science, University of Miami, Coral Gables, FL Surveys. Vol. 27, No. 4, December 1995 #12;520 q Dilip Sarkar I CONTENTS 1. 2 3. 4. 5. INTRODUCTION

Sarkar, Dilip

36

Tampa Electric Neural Network Sootblowing  

SciTech Connect (OSTI)

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

Mark A. Rhode

2004-09-30T23:59:59.000Z

37

Tampa Electric Neural Network Sootblowing  

SciTech Connect (OSTI)

Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NOx formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing 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

38

alters neural activation: Topics by E-print Network  

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

Structure-Activity Relationship (QSAR) using Artificial Neural in solving non-linear pattern classification problems, we propose several different models of neural networks...

39

activity evoked neural: Topics by E-print Network  

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

Structure-Activity Relationship (QSAR) using Artificial Neural in solving non-linear pattern classification problems, we propose several different models of neural networks...

40

attentional stability neural: Topics by E-print Network  

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

the Stability Region of a Neural Network with a General Distribution of Delays R Campbell, Sue Ann 38 A neural oscillator model of binaural auditory selective attention...

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

Imbibition well stimulation via neural network design  

DOE Patents [OSTI]

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

Weiss, William (Socorro, NM)

2007-08-14T23:59:59.000Z

42

NEURAL NETWORK RESIDUAL STOCHASTIC COSIMULATION FOR ENVIRONMENTAL DATA ANALYSIS  

E-Print Network [OSTI]

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

43

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

SciTech Connect (OSTI)

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

Dumidu Wijayasekara; Milos Manic; Piyush Sabharwall; Vivek Utgikar

2011-07-01T23:59:59.000Z

44

Self-organizing neural networks for learning air combat maneuvers  

E-Print Network [OSTI]

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

Tan, Ah-Hwee

45

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

SciTech Connect (OSTI)

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

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

2011-10-15T23:59:59.000Z

46

Auto-associative nanoelectronic neural network  

SciTech Connect (OSTI)

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

Nogueira, C. P. S. M.; Guimar„es, J. G. [Departamento de Engenharia Elťtrica - Laboratůrio de Dispositivos e Circuito Integrado, Universidade de BrasŪlia, CP 4386, CEP 70904-970 BrasŪlia DF (Brazil)

2014-05-15T23:59:59.000Z

47

E-Print Network 3.0 - artificial neural network-based Sample...  

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

tive-network-based... Lin, C.-T. and Lee, C. S. G. (1991). Neural-network-based fuzzy logic control and decision system... and artificial neural networks. In Eckmiller, R.,...

48

Using Neural Networks Atmospheric Model Physics  

E-Print Network [OSTI]

Outline · Background ­ GCM · Radiation · Convection ­ Neural Networks · NN Emulations of Existing Model · Major components of P = {R, W, C, T, S, CH}: ­ R - radiation (long & short wave processes): AER Inc ­ S ­ land, ocean, ice ­ air interaction ­ CH ­ chemistry (aerosols) · Components of P are 1-D

Anisimov, Mikhail

49

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

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

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

50

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

51

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

52

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

53

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

54

Learning in a hierarchical neural network  

E-Print Network [OSTI]

the sophisticated information processing, Fukushima has con- structed three types of cells in the network. These three types of cells perform Layer 0 Layer 1 Layer 2 Layer 3 X1 01 X2 X1 02 X2 03 Fig. 1 Operation of Multilayer Neural Network with Feedback... hyperlayer in Layer E. IS UMr ?? 16 (22) c& (hlaycr, num, hlayer', num') = I/ISUM& (23) at (hlayer, num, hlayer', num') = g0(1) ~ 1/ISUMr (24) The equations which for the synapses between layers 0 and 1 are shown in (22), (23) and (24). The ct synapses...

Michaelis, Matthew Clinton

2012-06-07T23:59:59.000Z

55

Optimized Learning with Bounded Error for Feedforward Neural Networks  

E-Print Network [OSTI]

Optimized Learning with Bounded Error for Feedforward Neural Networks A. Alessandri, M. Sanguineti-based learnings. A. Alessandri is with the Naval Automatio

Maggiore, Manfredi

56

artificial neural analysis: Topics by E-print Network  

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

Databases and Resources Websites Summary: Applications of Artificial Neural Networks and Fuzzy Models in High Throughput Screening to the existing HTS method, via Quantitative...

57

acid promotes neural: Topics by E-print Network  

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

discrimination of benign from malignant gastric neural network classifier, an efficient pattern recognition approach, is used to classify benign Likas, Aristidis 103 Deep...

58

aerosolised porcine neural: Topics by E-print Network  

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

discrimination of benign from malignant gastric neural network classifier, an efficient pattern recognition approach, is used to classify benign Likas, Aristidis 94 Deep...

59

adaptive neural control: Topics by E-print Network  

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

Summary: Fundamental developments in feedfonvard artificial neural net-works from the past thirty years are reviewed. The central theme of this paper is a description of the...

60

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

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

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

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

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

E-Print Network [OSTI]

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

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

1999-12-19T23:59:59.000Z

62

Safety Criteria and Safety Lifecycle for Artificial Neural Networks  

E-Print Network [OSTI]

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

Kelly, Tim

63

Safety Lifecycle for Developing Safety Critical Artificial Neural Networks  

E-Print Network [OSTI]

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

Kelly, Tim

64

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 introduce our tool for the optimisation of parameterised solar thermal power plants, and report the applicability of our approach. Keywords: Optimization, Solar thermal power plants, Neural networks, Genetic

√Ābrah√°m, Erika

65

Developmental Plasticity in Cartesian Genetic Programming Artificial Neural Networks  

E-Print Network [OSTI]

Developmental Plasticity in Cartesian Genetic Programming Artificial Neural Networks Maryam Mahsal developmental plasticity in Artificial Neural Networks using Carte- sian Genetic Programming. This is inspired by developmental plasticity that exists in the biological brain allowing it to adapt to a changing environment

Fernandez, Thomas

66

Apple Defect Segmentation by Artificial Neural Networks Devrim Unay a  

E-Print Network [OSTI]

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

Dupont, Stéphane

67

Arrhythmia Identification from ECG Signals with a Neural Network  

E-Print Network [OSTI]

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

Madden, Michael

68

Desynchronization in diluted neural networks  

SciTech Connect (OSTI)

The dynamical behavior of a weakly diluted fully inhibitory network of pulse-coupled spiking neurons is investigated. Upon increasing the coupling strength, a transition from regular to stochasticlike regime is observed. In the weak-coupling phase, a periodic dynamics is rapidly approached, with all neurons firing with the same rate and mutually phase locked. The strong-coupling phase is characterized by an irregular pattern, even though the maximum Lyapunov exponent is negative. The paradox is solved by drawing an analogy with the phenomenon of 'stable chaos', i.e., by observing that the stochasticlike behavior is 'limited' to an exponentially long (with the system size) transient. Remarkably, the transient dynamics turns out to be stationary.

Zillmer, Ruediger [INFN Sezione Firenze, via Sansone 1, I-50019 Sesto Fiorentino (Italy); Livi, Roberto [Dipartimento di Fisica, Universita di Firenze, via Sansone 1, I-50019 Sesto Fiorentino (Italy); Sezione INFN, Unita' INFM e Centro Interdipartimentale per lo Studio delle Dinamiche Complesse, via Sansone 1, I-50019 Sesto Fiorentino (Italy); Politi, Antonio; Torcini, Alessandro [Istituto dei Sistemi Complessi, CNR, CNR, via Madonna del Piano 10, I-50019 Sesto Fiorentino (Italy); Centro Interdipartimentale per lo Studio delle Dinamiche Complesse, via Sansone 1, I-50019 Sesto Fiorentino (Italy)

2006-09-15T23:59:59.000Z

69

NeuDetect: A Neural Network Data Mining Wireless Network Intrusion Detection System  

E-Print Network [OSTI]

NeuDetect: A Neural Network Data Mining Wireless Network Intrusion Detection System C.I. Ezeife wireless intrusion detection systems, this paper presents a method of applying artificial neural networks mining clas- sification technique to wireless network intrusion detection system. The proposed system

Ezeife, Christie

70

E-Print Network 3.0 - action potential back-propagation Sample...  

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

Global Minimum for Active Contour Models: A Minimal Path Approach Summary: (minimal geodesic). 3 Paths of Minimal Action Given some potential P that takes lower values near the...

71

Physical Parameterization of Stellar Spectra: The Neural Network Approach  

E-Print Network [OSTI]

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

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

1997-08-22T23:59:59.000Z

72

Digital neural network-based modeling technique for extrusion processes  

E-Print Network [OSTI]

and market conditions. In order to develop reliable and well-performing advanced process monitoring and diagnostic systems for achieving improved product quality and cost-effective operation, the neural network-based modeling technique for the extrusion...

Jang, Won-Hyouk

2001-01-01T23:59:59.000Z

73

RESERVOIR INFLOW FORECASTING USING NEURAL NETWORKS CHANDRASHEKAR SUBRAMANIAN  

E-Print Network [OSTI]

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

Manry, Michael

74

Neural networks in neuroscience: a brief overview Samuel Johnson1  

E-Print Network [OSTI]

1 Neural networks in neuroscience: a brief overview Samuel Johnson1 Instituto Carlos I de Física as a particular configuration of activities, just as a computer might store an image as a 1 samuel

Johnson, Samuel

75

Artificial Neural Network Circuit for Spectral Pattern Recognition  

E-Print Network [OSTI]

classification problems in Computer Science like handwriting recognition to cancer classification problems in Biomedical Engineering. The parallelism inherent in neural networks makes hardware a good choice to implement ANNs compared to software...

Rasheed, Farah

2013-09-04T23:59:59.000Z

76

Model building in neural networks with hidden Markov models†  

E-Print Network [OSTI]

This thesis concerns the automatic generation of architectures for neural networks and other pattern recognition models comprising many elements of the same type. The requirement for such models, with automatically ...

Wynne-Jones, Michael

77

Neural network calibration for miniature multi-hole pressure probes  

E-Print Network [OSTI]

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

Vijayagopal, Rajesh

1998-01-01T23:59:59.000Z

78

A neural network approach to burn-in  

E-Print Network [OSTI]

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

Clifford, Nancy Lynn

2012-06-07T23:59:59.000Z

79

artificial neural computation: Topics by E-print Network  

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

19 20 21 22 23 24 25 Next Page Last Page Topic Index 1 A RECONFIGURABLE COMPUTING ARCHITECTURE FOR IMPLEMENTING ARTIFICIAL NEURAL NETWORKS ON FPGA Engineering Websites Summary: A...

80

Experimental results of a predictive neural network HVAC controller  

SciTech Connect (OSTI)

Proportional, integral, and derivative (PID) control is widely used in many HVAC control processes and requires constant attention for optimal control. Artificial neural networks offer the potential for improved control of processes through predictive techniques. This paper introduces and shows experimental results of a predictive neural network (PNN) controller applied to an unstable hot water system in an air-handling unit. Actual laboratory testing of the PNN and PID controllers show favorable results for the PNN controller.

Jeannette, E.; Assawamartbunlue, K.; Kreider, J.F. [Univ. of Colorado, Boulder, CO (United States); Curtiss, P.S. [Architectural Energy Corp., Boulder, CO (United States)

1998-12-31T23:59:59.000Z

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.


81

A neural network approach to snack quality evaluation  

E-Print Network [OSTI]

A NEURAL NETWORK APPROACH TO SNACK QUALITY' EVALUATION A Thesis by MOHAMMAD SHAHEEN SAYEED Submitted to the Office of Graduate Studies of Texas ARM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE... May 1994 Major Subject: Electrical Engineering A NEURAL NETWORK APPROACH TO SNACK QUALITY EVALUATION A Thesis by MOHAMMAD SHAHEEN SAYEED Submitted to Texas ARM University in partial fulfillment of the requirements for the degree of MASTER...

Sayeed, Mohammad Shaheen

1994-01-01T23:59:59.000Z

82

A new acceleration technique for the backpropagation neural network paradigm  

E-Print Network [OSTI]

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

Pathak, Jogen K

1991-01-01T23:59:59.000Z

83

A portable neural network approach to vehicle tracking  

E-Print Network [OSTI]

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

Miller, Kelly Maxwell

1994-01-01T23:59:59.000Z

84

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

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

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

85

E-Print Network 3.0 - artificial neural networks-particle Sample...  

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

in the Netherlands. Summary: . and Burkhardt, H.: Vision-guided flame control using fuzzy-logic and neural networks, Particle Particle Systems... . Printed in the Netherlands....

86

Neural Network Based Intelligent Sootblowing System  

SciTech Connect (OSTI)

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

87

A conjugate gradient learning algorithm for recurrent neural networks  

E-Print Network [OSTI]

]. In particular, the conjugate gradient method is commonly used in training BP networks due to its speed1 A conjugate gradient learning algorithm for recurrent neural networks (Revised Version) Wing algorithm by incorporating conjugate gradient computation into its learning procedure. The resulting

Mak, Man-Wai

88

ID3, SEQUENTIAL BAYES, NAIVE BAYES AND BAYESIAN NEURAL NETWORKS  

E-Print Network [OSTI]

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

Kononenko, Igor

89

APPLICATION OF THE FUZZY MIN-MAX NEURAL NETWORK CLASSIFIER  

E-Print Network [OSTI]

. The fuzzy min-max classi cation network constitutes a promisimg pattern recognition approach that is based. Experimental results us- ing the modi ed model on a di cult pattern recognition prob- lem establishes of the fuzzy min-max clas- si cation neural network on a pattern recognition problem that involves both

Blekas, Konstantinos

90

APPLICATION OF THE FUZZY MINMAX NEURAL NETWORK CLASSIFIER  

E-Print Network [OSTI]

. The fuzzy min­max classification network consti­ tutes a promisimg pattern recognition approach dimensions. Experi­ mental results using the modified model on a difficult pattern recognition problem of the fuzzy min­max classi­ fication neural network on a pattern recognition problem that involves both

Likas, Aristidis

91

APPLICATION OF THE FUZZY MINMAX NEURAL NETWORK CLASSIFIER  

E-Print Network [OSTI]

. The fuzzy min­max classification network constitutes a promisimg pattern recognition approach that is based. Experimental results us­ ing the modified model on a difficult pattern recognition prob­ lem establishes of the fuzzy min­max clas­ sification neural network on a pattern recognition problem that involves both

Blekas, Konstantinos

92

Biomimetic interfacial interpenetrating polymer networks control neural stem cell behavior  

E-Print Network [OSTI]

Biomimetic interfacial interpenetrating polymer networks control neural stem cell behavior Krishanu polymer network (IPN), we define a robust synthetic and highly-defined plat- form for the culture of adult precisely orchestrate signal presentation to stem cells. Using a biomimetic interfacial interpenetrating

Saha, Krishanu

93

Self-teaching neural network learns difficult reactor control problem  

SciTech Connect (OSTI)

A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits.

Jouse, W.C.

1989-01-01T23:59:59.000Z

94

Neural Network Based Energy Storage System Modeling for Hybrid Electric Vehicles  

SciTech Connect (OSTI)

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

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

1999-08-01T23:59:59.000Z

95

Neural network tool for rapid recovery of plasma topology V. Tribaldos and B. Ph. van Milligen  

E-Print Network [OSTI]

Neural network tool for rapid recovery of plasma topology V. Tribaldos and B. Ph. van Milligen of neural networks as fitting tools is described; examples of the method for a D-shaped tokamak with an X and general conclusions are drawn. II. USING NEURAL NETWORKS AS FITTING TOOLS The major problem of fitting

van Milligen, Boudewijn

96

Eye Identification for Face Recognition with Neural Networks , Christer Jahren1)  

E-Print Network [OSTI]

Eye Identification for Face Recognition with Neural Networks √?ge Eide1) , Christer Jahren1) , Stig is approached using a two-stage neural network implemented in software. The first net finds the eyes of a person and the second neural network uses an image of the area around the eyes to identify the person. In a second

Haviland, David

97

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

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

98

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

99

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

100

Neural network definitions of highly predictable protein secondary structure classes  

SciTech Connect (OSTI)

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

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

1994-02-01T23:59:59.000Z

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

An artificial neural network controller for intelligent transportation systems applications  

SciTech Connect (OSTI)

An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

Vitela, J.E.; Hanebutte, U.R.; Reifman, J. [Argonne National Lab., IL (United States). Reactor Analysis Div.

1996-04-01T23:59:59.000Z

102

An analog time-multiplexing cellular neural networks computer  

E-Print Network [OSTI]

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

Fong, Apollo Quan

1995-01-01T23:59:59.000Z

103

Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks  

SciTech Connect (OSTI)

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

Nelson Butuk

2004-12-01T23:59:59.000Z

104

Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks  

SciTech Connect (OSTI)

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

Ziaul Huque

2007-08-31T23:59:59.000Z

105

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

106

Suitability of Fuzzy Systems and Neural Networks for Industrial Applications  

E-Print Network [OSTI]

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

Wilamowski, Bogdan Maciej

107

Wind Power Plant Prediction by Using Neural Networks: Preprint  

SciTech Connect (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

108

Neural Networks for Post-processing Model Output: Caren Marzban  

E-Print Network [OSTI]

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

Marzban, Caren

109

Neural Network-Based Accelerators for Transcendental Function Approximation  

E-Print Network [OSTI]

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

Joshi, Ajay

110

Successful neural network projects at the Idaho National Engineering Laboratory  

SciTech Connect (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

111

Electric Power System Anomaly Detection Using Neural Networks  

E-Print Network [OSTI]

normal system beha- viour at substations level, and raising an alarm signal when an abnormal status is detected; the problem is addressed by the use of autoassociat- ive neural networks, reading substation, and their increasing complexity make them vulnerable to failures or to deliberate attacks. Our goal is to detect

Tronci, Enrico

112

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

E-Print Network [OSTI]

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

Bullinaria, John

113

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

E-Print Network [OSTI]

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

Tino, Peter

114

Neural Networks ensemble for quality monitoring , M. Noyel1  

E-Print Network [OSTI]

tools of Total Quality Management (TQM). The American Production and Inventory Control Society (APICS) defines Total Quality Management as "A management approach to long-term success through customerNeural Networks ensemble for quality monitoring P. Thomas1 , M. Noyel1 , M.C. Suhner1 , P

Boyer, Edmond

115

Adaptive Blind Signal Processing--Neural Network Approaches  

E-Print Network [OSTI]

Adaptive Blind Signal Processing--Neural Network Approaches SHUN-ICHI AMARI, FELLOW, IEEE are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We dis- cuss recent developments

Vialatte, FranÁois

116

Hybrid coupled modeling of the tropical Pacific using neural networks  

E-Print Network [OSTI]

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

Hsieh, William

117

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

E-Print Network [OSTI]

SDTC Neural Network Traction Control of an Electric Vehicle without Differential Gears A. Haddoun1 network traction control approach of an Electric vehicle (EV) without differential gears (electrical that the proposed SDTC neural network approach operates satisfactorily. Keywords--Electric vehicle propulsion

Paris-Sud XI, Universitť de

118

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

E-Print Network [OSTI]

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

Arleo, Angelo

119

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

120

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

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

Laser programmable integrated circuit for forming synapses in neural networks  

DOE Patents [OSTI]

Customizable neural network in which one or more resistors form each synapse is disclosed. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength. 5 figs.

Fu, C.Y.

1997-02-11T23:59:59.000Z

122

Laser programmable integrated curcuit for forming synapses in neural networks  

DOE Patents [OSTI]

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

Fu, Chi Y. (San Francisco, CA)

1997-01-01T23:59:59.000Z

123

Process for forming synapses in neural networks and resistor therefor  

DOE Patents [OSTI]

Customizable neural network in which one or more resistors form each synapse is disclosed. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength. 5 figs.

Fu, C.Y.

1996-07-23T23:59:59.000Z

124

Process for forming synapses in neural networks and resistor therefor  

DOE Patents [OSTI]

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

Fu, Chi Y. (San Francisco, CA)

1996-01-01T23:59:59.000Z

125

arginylation-dependent neural crest: Topics by E-print Network  

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

discrimination of benign from malignant gastric neural network classifier, an efficient pattern recognition approach, is used to classify benign Likas, Aristidis 140 Deep...

126

Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing in Wireless Sensor Networks  

E-Print Network [OSTI]

There are many challenges when designing and deploying wireless sensor networks (WSNs). One of the key challenges is how to make full use of the limited energy to prolong the lifetime of the network, because energy is a valuable resource in WSNs. The status of energy consumption should be continuously monitored after network deployment. In this paper, we propose coverage and connectivity aware neural network based energy efficient routing in WSN with the objective of maximizing the network lifetime. In the proposed scheme, the problem is formulated as linear programming (LP) with coverage and connectivity aware constraints. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage and connectivity aware routing with data transmission. The proposed scheme is compared with existing schemes with respect to the parameters such as number of alive nodes, packet delivery fraction, and node residual energy. The simulation results show that the proposed scheme can be used in wi...

,; Kumar, Manoj; Patel, R B; 10.5121/jgraphhoc.2010.2105

2010-01-01T23:59:59.000Z

127

An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: Application for isolated sites in Algeria  

E-Print Network [OSTI]

An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: Application for isolated sites in Algeria

Mellit, A; Hadj-Arab, A; Guessoum, A

2004-01-01T23:59:59.000Z

128

Neural Networks and Expert Systems to solve the problems of large amounts of Experimental Data at JET  

E-Print Network [OSTI]

Neural Networks and Expert Systems to solve the problems of large amounts of Experimental Data at JET

129

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

E-Print Network [OSTI]

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

Martinez, Tony R.

130

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

E-Print Network [OSTI]

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

Bauer, Niels Konrad

1988-01-01T23:59:59.000Z

131

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

E-Print Network [OSTI]

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

Duong, Timothy Q.

132

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

E-Print Network [OSTI]

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

Tolbert, Leon M.

133

Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern  

E-Print Network [OSTI]

Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern Classification-Gurion University, Beer-Sheva 84105, Israel Abstract. Since its inception in 1992, the fuzzy ARTMAP (FAM) neural network (NN) has attracted researchers' attention as a fast, accurate, off and online pattern classifier

Lerner, Boaz

134

Evolutionary Strategies and Genetic Algorithms for Dynamic Parameter Optimization of Evolving Fuzzy Neural Networks  

E-Print Network [OSTI]

Fuzzy Neural Networks are usually used to model evolving processes, which are developing and changing easier the evolving processes modeling task. Evolving Fuzzy Neural Networks (EFuNNs) [Kasabov 2001] formEvolutionary Strategies and Genetic Algorithms for Dynamic Parameter Optimization of Evolving Fuzzy

Yao, Xin

135

Designing Polymer Blends Using Neural Networks, Genetic Algorithms, and Markov Chains  

E-Print Network [OSTI]

1 Designing Polymer Blends Using Neural Networks, Genetic Algorithms, and Markov Chains N. K. Roy1 potential candidates for blending using Neural Networks. Generally the parent polymers of the blend need systems like branched polymers, high molecular weight polymer mixtures, block copolymers, interpenetrating

Potter, Don

136

Neural network analysis of strength and ductility of welding alloys for high strength low  

E-Print Network [OSTI]

Neural network analysis of strength and ductility of welding alloys for high strength low alloy There are considerable demands for the development of weld metals for high strength low alloy steels. To assist in meeting such demands, a neural network was trained and tested on a set of data obtained on weld metals

Cambridge, University of

137

Analysis, Modeling and Neural Network Traction Control of an Electric Vehicle  

E-Print Network [OSTI]

Analysis, Modeling and Neural Network Traction Control of an Electric Vehicle without Differential Terms--Electric vehicle, electric motor, speed estimation, neural networks, traction control. I. INTRODUCTION Recently, Electric Vehicles (EVs) including fuel-cell and hybrid vehicles have been developed very

Paris-Sud XI, Universitť de

138

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

E-Print Network [OSTI]

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

Catholic University of Chile (Universidad Católica de Chile)

139

Performance of neural networks in materials H. K. D. H. Bhadeshia*1,2  

E-Print Network [OSTI]

, Uncertainties, Errors Introduction Neural networks have proved to be powerful and popular in dealing pointed out, when discussing neural networks in the hydro- sciences,8 that in many cases the model building process is described poorly, making it difficult to assess the optimality of the results obtained

Cambridge, University of

140

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

E-Print Network [OSTI]

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

Boyer, Edmond

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

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

E-Print Network [OSTI]

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

MacIver, Malcolm A.

142

Polynomial-Neural-Networks--B ased Mobile Robot Path C. L. Philip Chen and Farid Ahmed  

E-Print Network [OSTI]

is then utilized to move the robot away from the obstacle or direct the rol)Ot along the contour of the obstaclePolynomial- Neural-Networks--B ased Mobile Robot Path Planning C. L. Philip Chen and Farid Ahmed for mobile robot navigation. The PNN is a feature-based mapping neural network which can be successfully

Ahmed, Farid

143

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

E-Print Network [OSTI]

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

Kaber, David B.

144

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

E-Print Network [OSTI]

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

Lee, Hyowon

145

Process Planning Using An Integrated Expert System And Neural Network Approach  

E-Print Network [OSTI]

Process Planning Using An Integrated Expert System And Neural Network Approach 1 Mark Wilhelm-9209424. 2 Corresponding author. #12;Process Planning Using An Integrated Expert System And Neural Network a unique computer aided process planner for metal furniture assembly, welding and painting using a rule

Smith, Alice E.

146

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

147

Nonlinear adaptive internal model control using neural networks  

E-Print Network [OSTI]

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

Gandhi, Amit Krushnavadan

2012-06-07T23:59:59.000Z

149

Self-organizing neural network as a fuzzy classifier  

SciTech Connect (OSTI)

This paper describes a self-organizing artificial neural network, based on Kohonen`s model of self-organization, which is capable of handling fuzzy input and of providing fuzzy classification. Unlike conventional neural net models, this algorithm incorporates fuzzy set-theoretic concepts at various stages. The input vector consists of membership values for linguistic properties along with some contextual class membership information which is used during self-organization to permit efficient modeling of fuzzy (ambiguous) patterns. A new definition of gain factor for weight updating is proposed. An index of disorder involving mean square distance between the input and weight vectors is used to determine a measure of the ordering of the output space. This controls the number of sweeps required in the process. Incorporation of the concept of fuzzy partitioning allows natural self-organization of the input data, especially when they have ill-defined boundaries. The output of unknown test patterns is generated in terms of class membership values. Incorporation of fuzziness in input and output is seen to provide better performance as compared to the original Kohonen model and the hard version. The effectiveness of this algorithm is demonstrated on the speech recognition problem for various network array sizes, training sets and gain factors. 24 refs.

Mitra, S.; Pal, S.K. [Indian Statistical Inst., Calcutta (India)] [Indian Statistical Inst., Calcutta (India)

1994-03-01T23:59:59.000Z

150

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

SciTech Connect (OSTI)

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

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

2008-06-12T23:59:59.000Z

151

Pre-synaptic Lateral Inhibition Provides a Better Architecture for Self-Organising Neural Networks  

E-Print Network [OSTI]

Unsupervised learning is an important property of the brain and of many artificial neural networks. A large variety of unsupervised learning algorithms have been proposed. This paper takes a different approach in considering the architecture of the neural network rather than the learning algorithm. It is shown that a self-organising neural network architecture using pre-synaptic lateral inhibition enables a single learning algorithm to find distributed, local, and topological representations as appropriate to the structure of the input data received. It is argued that such an architecture not only has computational advantages but is a better model of cortical self-organisation.

1999-01-01T23:59:59.000Z

152

Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31 -August 4, 2005 Facilitatory Neural Activity Compensating for  

E-Print Network [OSTI]

they are actually co-localized. This phenomenon may be due to motion extrapolation: The nervous system has internal and the flashed bar due to such a discrepancy in extrapolation. However, the motion extrapolation model has someProceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31

Choe, Yoonsuck

153

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

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

154

Drive reinforcement neural networks for reactor control. Final report  

SciTech Connect (OSTI)

In view of the loss of the third year funding, the scope of the project goals has been revised. The revision in project scope no longer allows for the detailed modeling of the EBR-11 start-up task that was originally envisaged. The authors are continuing, however, to model the control of the rapid power ascent of the University of Arizona TRIGA reactor using a model-based controller and using a drive reinforcement neural network. These will be combined during the concluding period of the project into a hierarchical control architecture. In addition, the modeling of a PWR feedwater heater has continued, and an autonomous fault-tolerant software architecture for its control has been proposed.

Williams, J.G.; Jouse, W.C.

1995-02-01T23:59:59.000Z

155

APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION  

SciTech Connect (OSTI)

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

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

2012-11-01T23:59:59.000Z

156

Active control of SDF systems using artificial neural networks.  

SciTech Connect (OSTI)

A study of the application of artificial neural networks (ANNs) to active structural control is presented. A simple effective strategy for the on-line control of single-degree-of-freedom (SDF) structures is proposed. The strategy is to apply the control force at every time step to destroy the buildup of the system response, and the control force needed for the next time step is fully determined from the information available at the current time ; therefore the time delay associated with the control algorithm is eliminated. The control algorithm can be implemented for either a closed or open-closed loop controller. The controller uses a trained ANN to determine the control force such that the velocity induced at the preceding time step is canceled. A feedforward neural network with an adaptive backpropagation training method is used in this study. In the backpropagation training, the learning rate is determined by ensuring the decrease of the error function of the input-output training patterns at each training cycle. Numerical examples of SDF systems under earthquake excitations are given to illustrate the effectiveness of the proposed control strategy. The uncertainties in the time history of the excitation and in the modeling of the system, including the magnitudes of the excitations, the natural frequency and nonlinearity of the systems are examined. Significant reduction of the response is observed. Also, is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history, an ability that linear control laws lack.

Tang, Y.; Reactor Engineering

1997-01-01T23:59:59.000Z

157

PkANN: Non-Linear Matter Power Spectrum Interpolation through Artificial Neural Networks  

E-Print Network [OSTI]

We investigate the interpolation of power spectra of matter fluctuations using artificial neural networks (ANNs). We present a new approach to confront small-scale non-linearities in the matter power spectrum. This ...

Agarwal, Shankar

2012-12-31T23:59:59.000Z

158

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

159

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

160

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

E-Print Network [OSTI]

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

Kao, Ling-Jing

2001-01-01T23:59:59.000Z

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

Computational Fracture Prediction in Steel Moment Frame Structures with the Application of Artificial Neural Networks  

E-Print Network [OSTI]

networks, the element with the highest potential to initiate a fatigue crack is identified, and the time elapsed up to the crack initiation is predicted and compared with its true synthetic answer. Following similar procedures, a competitive neural array...

Long, Xiao

2012-10-19T23:59:59.000Z

162

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

E-Print Network [OSTI]

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

Cambridge, University of

163

Simulation and Synthesis of Arti cial Neural Networks Using Data ow Models in Ptolemy  

E-Print Network [OSTI]

to Homogeneous Synchronous Data ow SDF models. We combine Boolean Data ow BDF and SDF models to model Cellular Neural Networks CNNs. By modeling DSP oper- ations in SDF, we are free to mix ANNs and DSP subsys- tems

Evans, Brian L.

164

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

E-Print Network [OSTI]

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

Johnson, M. L.

165

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

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

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

166

Fast curve fitting using neural networks C. M. Bishop and C. M. Roach  

E-Print Network [OSTI]

Fast curve fitting using neural networks C. M. Bishop and C. M. Roach Citation: Rev. Sci. Instrum Kingdom C. M. Roach AEA Technology,Culham Laboratory, (Euratom/UkAEA Fusion Association)Oxon OX14 3DB

167

An Analysis Method for Operations of Hot Water Heaters by Artificial Neural Networks  

E-Print Network [OSTI]

Authors tried to apply an Artificial Neural Network (ANN) to estimation of state of building systems. The systems used in this study were gas combustion water heaters. Empirical equations to estimate gas consumption from measureble properies...

Yamaha, M.; Takahashi, M.

2004-01-01T23:59:59.000Z

168

Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction  

SciTech Connect (OSTI)

In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.

Zemouri, Ryad [Laboratoire d'automatique, CNAM, 21 rue Pinel, 75013 Paris (France); Racoceanu, Daniel [IPAL, UMI CNRS 2955, UJF, I2R/A-STAR, NUS, 1 Fusionopolis Way, 21-01 Connexis, 138632 Singapore (Singapore); Zerhouni, Noureddine [FEMTO-ST-UMR CNRS 6174, ENSMM, UFC, UTBM, 32 Avenue de l'Observatoire, 25044 Besancon (France); Minca, Eugenia [Faculty of Electric Engineering, Valahia University, Bd. Unirii, nr. 18, 0200, Targoviste (Romania); Filip, Florin [Romanian Academy, Calea Victoriei 125, Sct. 1, Bucuresti (Romania)

2009-03-05T23:59:59.000Z

169

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

E-Print Network [OSTI]

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

Chang, Joongho

1994-01-01T23:59:59.000Z

170

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

171

A neural network model for predicting the silicon content of the hot metal at No. 2 blast furnace of SSAB Luleaa  

SciTech Connect (OSTI)

To predict the silicon content of hot metal at No. 2 blast furnace, SSAB, Luleaa Works, a three-layer Back-Propagation network model has been established. The network consists of twenty-eight inputs, six middle nodes and one output and uses a generalized delta rule for training. Different network structures and different training strategies have been tested. A well-functioning network with dynamic updating has been designed. The off-line test and the on-line application results showed that more than 80% of the predictions can match the actual silicon content in hot metal in a normal operation, if the allowable prediction error was set to {+-}0.05% Si, while the actual fluctuation of the silicon content was larger than {+-}0.10% Si.

Zuo Guangqing; Ma Jitang; Bo, B. [Luleaa Univ. of Technology (Sweden). Div. of Process Metallurgy

1996-12-31T23:59:59.000Z

172

Combining a recurrent neural network and a PID controller for prognostic purpose  

E-Print Network [OSTI]

Combining a recurrent neural network and a PID controller for prognostic purpose A way to improve Basis Function network (RRBF) and a proportional integral derivative controller (PID) is proposed in order to improve the accuracy of predictions. The PID controller attempts to correct the error between

Paris-Sud XI, Université de

173

Application of an artificial neural network to reactor core analysis  

SciTech Connect (OSTI)

To analyze three-dimensional reactor core behaviors, the finite difference or the finite element method have generally been used. Nodal method is adopted as another tool for analyzing transient core characteristics. These methods, however, require much calculation time to solve very complicated iterations for better convergence. Especially when the transient states are to be predicted, none of these methods can meet the requirements within the time span in which the operator can react. To overcome these difficulties, a new analytic model based on the artificial neural networks (ANNs) is suggested. Because trained ANNs are capable of modeling the input/output relationships of a nonlinear system without complex analogy, they are able to map the power distributions and calculate the eigenvalue corresponding to the core conditions in a short time and utilize the previous results by updating the weights of inter-connection between input and output patterns. To confirm the accuracy and capability, daily load-follow operation in a pressurized water reactor (PWR) is simulated using the new analytic model.

Seung Hwan Seong; Un Chul Lee [Seoul National Univ. (Korea, Republic of)

1995-12-31T23:59:59.000Z

174

Using artificial neural network tools to analyze microbial biomarker data  

SciTech Connect (OSTI)

A major challenge in the successful implementation of bioremediation is understanding the structure of the indigenous microbial community and how this structure is affected by environmental conditions. Culture-independent approaches that use biomolecular markers have become the key to comparative microbial community analysis. However, the analysis of biomarkers from environmental samples typically generates a large number of measurements. The large number and complex nonlinear relationships among these measurements makes conventional linear statistical analysis of the data difficult. New data analysis tools are needed to help understand these data. We adapted artificial neural network (ANN) tools for relating changes in microbial biomarkers to geochemistry. ANNs are nonlinear pattern recognition methods that can learn from experience to improve their performance. We have successfully applied these techniques to the analysis of membrane lipids and nucleic acid biomarker data from both laboratory and field studies. Although ANNs typically outperform linear data analysis techniques, the user must be aware of several considerations and issues to ensure that analysis results are not misleading: (1) Overfitting, especially in small sample size data sets; (2) Model selection; (3) Interpretation of analysis results; and (4) Availability of tools (code). This poster summarizes approaches for addressing each of these issues. The objectives are: (1) Develop new nonlinear data analysis tools for relating microbial biomolecular markers to geochemical conditions; (2) Apply these nonlinear tools to field and laboratory studies relevant to the NABIR Program; and (3) Provide these tools and guidance in their use to other researchers.

Brandt, C.C.; Schryver, J.C.; Almeida, J.S.; Pfiffner, S.M.; Palumbo, A.V.

2004-03-17T23:59:59.000Z

175

Evidence for single top quark production using Bayesian neural networks  

SciTech Connect (OSTI)

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

176

Protein Classification Artificial Neural System: A filter program for database search  

SciTech Connect (OSTI)

A neural network classification method has been developed as an alternative approach to the large database search/organization problem. The system, termed Protein Classification Artificial Neural System (ProCANS), is implemented on a Cray Y-MP8/864 supercomputer for rapid superfamily classification of unknown proteins based on the information content of the neural interconnections. The system employs an n-gram hashing function for sequence encoding and modular back-propagation networks for classification. The system was developed with the first 2,724 entries in 690 superfamilies of the annotated PIR (Protein Identification Resource) protein sequence database. Three prediction sets were used to evaluate the system performance. The first consists of 651 annotated entries randomly chosen from the 690 superfamilies. The second set consists of 482 unclassified entries from the preliminary PIR database, whose superfamilies were identified by the fasta, blastp and sp database search methods. The third set is a subset of data set 2 with only superfamilies of more than 20 entries. At a low cut-off score of 0.01, the sensitivity is 92, 82 and 100%, respectively, for the three prediction sets. At a high cut-off score of 0.9, on the other hand, a close to 100% specificity is achieved with a reduced sensitivity.

Wu, C.H.; Wang, C.C.; Yazdanpanahi, I. [Univ. of Texas Health Center, Tyler, TX (United States)

1993-12-31T23:59:59.000Z

177

artificial neural nets: Topics by E-print Network  

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

Adaptive Engineering Websites Summary: Neural Nets for Adaptive Filtering and Adaptive Pattern Recognition Bernard Widrow, Stanford elements, on the other hand, arethe building...

178

adaptive neural coding: Topics by E-print Network  

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

examples and in an application to image compression. 1 Introduction The self Zachmann, Gabriel 42 Adaptive Control Using Combined Online and Background Learning Neural...

179

adapted neural population: Topics by E-print Network  

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

examples and in an application to image compression. 1 Introduction The self Zachmann, Gabriel 24 Adaptive Control Using Combined Online and Background Learning Neural...

180

Neural network technology for automatic fracture detection in sonic borehole image data  

E-Print Network [OSTI]

. 3 Neural Network Training and Testing VI. EXPERIMENTAL DESIGN AND RESULTS . ?39 . . . 40 . . . 44 46 VI. I Experimental Design Vl. l. I Expert Evaluation of the BHTV-Data VI. 1. 2 Design of the Neural System Experiment VI. 1. 3 Description... of the BHTV-Data Representations . . . VI. 2 Experimental Results V1. 3 Summary and Discussion VII. CONCLUSION AND FUTURE WORK . . . 46 . . . . 48 . 50 . . . 57 . . . 89 Vll. I Model-Based Recognition . . . VII. 2 Intelligent Neurocontrol...

Schnorrenberg, Frank Theo

1992-01-01T23:59:59.000Z

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.


181

artificial neural net: Topics by E-print Network  

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

neural net First Page Previous Page 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Next Page Last Page Topic Index 1 On Combining Artificial Neural Nets CiteSeer...

182

1100 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 5, SEPTEMBER 2004 Temporal Codes and Computations for Sensory  

E-Print Network [OSTI]

potential implications of temporal codes and computations for new kinds of neural networks are explored and computations that different temporal codes afford, rather than respective information transmission capac- ities1100 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 5, SEPTEMBER 2004 Temporal Codes

Cariani, Peter

183

Econometric and Neural Network Analysis of the Labor Productivity and Average Gross Earnings Indices in the Romanian Industry  

E-Print Network [OSTI]

Econometric and Neural Network Analysis of the Labor Productivity and Average Gross Earnings and models that were used consist of several lag econometric models, ARIMA processes, as well as feed forward AGEI and LPI. Key-Words: - labor productivity, econometric model, ARIMA, VAR, neural network, forecast

Paris-Sud XI, Universitť de

184

A neural network clustering algorithm for the ATLAS silicon pixel detector  

E-Print Network [OSTI]

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

ATLAS collaboration

2014-06-30T23:59:59.000Z

185

Use of neural networks in the operation of nuclear power plants  

SciTech Connect (OSTI)

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

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

1990-01-01T23:59:59.000Z

186

Tight bounds on the size of neural networks for classification problems  

SciTech Connect (OSTI)

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

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

1997-06-01T23:59:59.000Z

187

Computing single step operators of logic programming in radial basis function neural networks  

SciTech Connect (OSTI)

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I?I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

2014-07-10T23:59:59.000Z

188

acid neural cell: Topics by E-print Network  

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

We have seen that navigation requires knowledge of heading, and that HD cells in the brain actNEURAL MODELS OF HEAD-DIRECTION CELLS PETER ZEIDMAN JOHN A. BULLINARIA School of...

189

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

Open Energy Info (EERE)

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

190

Development of Ensemble Neural Network Convection Parameterizations for Climate Models  

SciTech Connect (OSTI)

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

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

2012-05-02T23:59:59.000Z

191

Design and Development of an Artificial Neural Network for Estimation of Formation Permeability  

E-Print Network [OSTI]

SPE 28237 Design and Development of an Artificial Neural Network for Estimation of Formation and measuring their oldest practices for estimating the formation permeability. Coring every well in a large, especially in fields with hundreds of wells, requires a large amount of capital. In a heterogeneous field

Mohaghegh, Shahab

192

DeepPose: Human Pose Estimation via Deep Neural Networks Alexander Toshev  

E-Print Network [OSTI]

benchmarks of diverse real-world images. 1. Introduction The problem of human pose estimation, defined-world problems. In this work we ascribe to this holistic view of human pose estimation. We capitalize on recentDeepPose: Human Pose Estimation via Deep Neural Networks Alexander Toshev toshev@google.com Google

Tomkins, Andrew

193

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

E-Print Network [OSTI]

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

Gasser, Michael

194

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

E-Print Network [OSTI]

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

Michel, Howard E.

195

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

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

196

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

E-Print Network [OSTI]

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

Dayan, Peter

197

A Fuzzy Neural Network Approach Based on Dirichlet Tesselations for Nearest Neighbor Classi cation of  

E-Print Network [OSTI]

-mail: andreas@theseas.ntua.gr Abstract A neural network classi er using fuzzy set representation of pattern concerning di cult recognition problems show that the proposed approach is very successful in applying fuzzy sets to pattern classi cation. 1 Introduction Several models have been developed during the last years

Blekas, Konstantinos

198

A Fuzzy Neural Network Approach Based on Dirichlet Tesselations for Nearest Neighbor Classification of  

E-Print Network [OSTI]

fuzzy sets to pattern classification. 1 Introduction Several models have been developed during the last­mail: andreas@theseas.ntua.gr Abstract A neural network classifier using fuzzy set representation of pattern this synergistic combination to building efficient pattern classifiers [5, 7, 9], as the application of fuzzy sets

Likas, Aristidis

199

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

E-Print Network [OSTI]

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

Vermont, University of

200

B-spline neural networks based PID controller for Hammerstein systems  

E-Print Network [OSTI]

B-spline neural networks based PID controller for Hammerstein systems X. Hong1 , S. Iplikci2 S, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Abstract. A new PID tuning and controller of a PID controller together with a correction term. In order to update the control signal, the multi- step

Chen, Sheng

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

A WhatandWhere Fusion Neural Network for Recognition and Tracking of Multiple Radar Emitters  

E-Print Network [OSTI]

is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type a high level of performance on complex, incomplete and overlapping radar data. #12; 1 Introduction RadarA What­and­Where Fusion Neural Network for Recognition and Tracking of Multiple Radar Emitters Eric

Grossberg, Stephen

202

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

E-Print Network [OSTI]

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

Coutinho, Alvaro L. G. A.

203

DESIGN OF FUEL-ADDITIVES USING HYBRID NEURAL NETWORKS AND EVOLUTIONARY  

E-Print Network [OSTI]

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

Venkatasubramanian, Venkat

204

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

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

205

Prediction of Interface Residues in ProteinProtein Complexes by a Consensus Neural Network Method: Test  

E-Print Network [OSTI]

Prediction of Interface Residues in Protein≠Protein Complexes by a Consensus Neural Network Method important information for predicting struc- tures of new protein complexes. This motivated us to develop the PPISP method for predicting inter- face residues in protein≠protein complexes. In PPISP, sequence

Weston, Ken

206

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

E-Print Network [OSTI]

, and operations e- ciency. Consequent to the primary recovery, water- ¬Įood is often used as a secondary recoveryNon-parametric regression and neural-network in¬ģll drilling recovery models for carbonate ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in West Texas

Valkó, Peter

207

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

E-Print Network [OSTI]

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

Slatton, Clint

208

PLA using RLSA and a neural network C. Strouthopoulos, N. Papamarkos *, C. Chamzas  

E-Print Network [OSTI]

PLA using RLSA and a neural network C. Strouthopoulos, N. Papamarkos *, C. Chamzas Electric by computers. An import- ant procedure in the digital processing of documents is the page layout analysis (PLA). The goal of the PLA is to discover the formatting of the text and, from that, to derive the meaning

Chamzas, Christodoulos

209

IEEE TRANSACTIONS ON NEURAL NETWORKS 1 Online Kernel-based Learning  

E-Print Network [OSTI]

an application in task-space tracking control of redundant robots possible. The model parametrization furtherIEEE TRANSACTIONS ON NEURAL NETWORKS 1 Online Kernel-based Learning for Task-Space Tracking Robot Control Duy Nguyen-Tuong, Jan Peters Abstract--Task-space control of redundant robot systems based

210

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

E-Print Network [OSTI]

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

Dupont, Stéphane

211

Apple Defect Detection and Quality Classification with MLP-Neural Networks  

E-Print Network [OSTI]

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

Dupont, Stéphane

212

Classification with Artificial Neural Networks and Support Vector Machines: application to oil fluorescence spectra  

E-Print Network [OSTI]

(WQI), and to signal predictions in a nuclear power plant (Kim WJ, S H Chang & B H Lee 1993). They haveClassification with Artificial Neural Networks and Support Vector Machines: application to oil, and Oil fluorescence ABSTRACT: This paper reports on oil classification with fluorescence spectroscopy

Oldenburg, Carl von Ossietzky Universität

213

Grid Cells: The Position Code, Neural Network Models of Activity, and the Problem of Learning  

E-Print Network [OSTI]

COMMENTARY Grid Cells: The Position Code, Neural Network Models of Activity, and the Problem on the modeling and theoretical fronts in the quest to unravel the computational properties of the grid cell code and to explain the mechanisms underlying grid cell dynamics. The goals of the review are to outline a coherent

Fiete, Ila

214

Instinct and Learning Synergy in Simulated Foraging Using a Neural Network Thomas E. Portegys  

E-Print Network [OSTI]

Instinct and Learning Synergy in Simulated Foraging Using a Neural Network Thomas E. Portegys simple animals learn, and is therefore a useful approach to simulating them. The way this works and experience are shown to form a potent combination to achieve effective foraging in a simulated environment

Portegys, Thomas E.

215

Exploring the Effects of Lamarckian and Baldwinian Learning in Evolving Recurrent Neural Networks  

E-Print Network [OSTI]

≠ winian learning mechanism''. discuss the results of the simulations. Finally, we conclude our findings1 Exploring the Effects of Lamarckian and Baldwinian Learning in Evolving Recurrent Neural Networks solution. In order to reduce the number of gener≠ ations taken, the Lamarckian learning mechanism

Mak, Man-Wai

216

Retrieval dynamics of neural networks constructed from local and nonlocal learning rules  

E-Print Network [OSTI]

than those constructed from local learning rules. However, extensive numerical simulations by Forrest223 Retrieval dynamics of neural networks constructed from local and nonlocal learning rules J locales sont comparťes ŗ l'aide de simulations numťriques et apparaissent trŤs similaires. Nos simulations

Paris-Sud XI, Universitť de

217

Forecasting of preprocessed daily solar radiation time series using neural networks  

E-Print Network [OSTI]

Forecasting of preprocessed daily solar radiation time series using neural networks Christophe prediction of global solar radiation on a horizontal surface. First results are promising with nRMSE ~ 21 t or at day d and year y d H0 Extraterrestrial solar radiation coefficient for day d [MJ/m²] xt, xd,y Time

Boyer, Edmond

218

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

E-Print Network [OSTI]

Transitional Modeling of Building Heating Energy Demand Using Artificial1 Neural Network2 Subodh Paudel a, it is39 essential to know energy flows and energy demand of the buildings for the control of heating and40 cooling energy production from plant systems. The energy demand of the building system, thus,41

Paris-Sud XI, Université de

219

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

E-Print Network [OSTI]

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

B. Cessac

2011-06-23T23:59:59.000Z

220

Side-Channel Resistance Evaluation of a Neural Network Based Lightweight Cryptography Scheme  

E-Print Network [OSTI]

Side-Channel Resistance Evaluation of a Neural Network Based Lightweight Cryptography Scheme Marc Email: koch@esa.cs.tu-darmstadt.de Abstract-- Side-channel attacks have changed the design of secure such as, e.g., AES, show the need to consider these aspects to build more resistant cryptographic systems

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

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

E-Print Network [OSTI]

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

Siegelmann , Hava T

222

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

E-Print Network [OSTI]

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

de Freitas, Nando

223

Neural Networks, ol 7, No 1, pp 183-194, 1994 Copyright 1994Elsevier Science Ltd  

E-Print Network [OSTI]

that acts on the setting of the steam admission valve of the unit turbine. It is of great importance over ttme A feedforward neural network is trained to control the steam admtsston valve of the turbme is slow and does not allow the control designer to take into account pos- sible nonlinearities

Widrow, Bernard

224

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

E-Print Network [OSTI]

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

Ohlsson, Mattias

225

Potential application of neural networks to the operation of nuclear power plants  

SciTech Connect (OSTI)

The application of neural networks, a rapidly evolving technology used extensively in defense applications, to some of the problems of operating nuclear power plants is a logical complement to the expert systems currently being introduced in some of those plants. The potential applications of neural networks include, but are not limited to: (1) Diagnosing specific abnormal conditions. (2) Identifying nonlinear dynamics and transients. (3) Detecting the change of mode of operation. (4) Controlling temperature and pressure during start-up. (5) validating signals. (6) Plant-wide monitoring using autoassociative neural networks. (7) Monitoring of check valves. (8) Modeling the plant thermodynamics to increase efficiency. (9) Emulating core reload calculations. (10) Analyzing temporal sequences in the U.S. Nuclear Regulatory Commission Licensee Event Reports. (11) Monitoring plant parameters. (12) Analyzing vibrations in plants and rotating machinery. The work on such applications indicates that neural networks alone, or in conjunction with other advanced technologies, have the potential to enhance the safety, reliability, and operability of nuclear power plants. 36 refs.

Uhrig, R.E. [University of Tennessee, Knoxville, TN (United States)]|[Oak Ridge National Laboratory, TN (United States)

1991-01-01T23:59:59.000Z

226

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

E-Print Network [OSTI]

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

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

2014-01-01T23:59:59.000Z

227

Neural Network forecasts of the tropical Pacific sea surface temperatures  

E-Print Network [OSTI]

. Hsieh Dept. of Earth and Ocean Sciences, University of British Columbia Vancouver, BC, Canada Benyang, Vancouver, BC V6T 1Z4, Canada; Phone: (604) 822- 2821, Fax: (604) 822-6088; Email: whsieh@eos.ubc.ca Running and decadal changes in the prediction skills in the NL and LR models were also studied. Keywords: neural

Hsieh, William

228

Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings  

E-Print Network [OSTI]

This study examines the performance and adaptability of Artificial Neural Network (ANN)-based thermal control strategies for diverse thermal properties of building envelope conditions applied to residential buildings. The thermal performance using...

Moon, Jin Woo; Chang, Jae D.; Kim, Sooyoung

2013-07-18T23:59:59.000Z

229

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

E-Print Network [OSTI]

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

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

2006-01-01T23:59:59.000Z

230

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

E-Print Network [OSTI]

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

Plaza, Antonio J.

231

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

232

High quality garbage: A neural network plastic sorter in hardware and software  

SciTech Connect (OSTI)

In order to produce pure polymer streams from post-consumer waste plastics, a quick, accurate and relatively inexpensive method of sorting needs to be implemented. This technology has been demonstrated by using near-infrared spectroscopy reflectance data and neural network classification techniques. Backpropagation neural network routines have been developed to run real-time sortings in the lab, using a laboratory-grade spectrometer. In addition, a new reflectance spectrometer has been developed which is fast enough for commercial use. Initial training and test sets taken with the laboratory instrument show that a network is capable of learning 100% when classifying 5 groups of plastic (HDPE and LDPE combined), and up to 100% when classifying 6 groups. Initial data sets from the new instrument have classified plastics into all seven groups with varying degrees of success. One of the initial networks has been implemented in hardware, for high speed computations, and thus rapid classification. Two neural accelerator systems have been evaluated, one based on the Intel 8017ONX chip, and another on the AT&T ANNA chip.

Stanton, S.L.; Alam, M.K.; Hebner, G.A.

1993-09-01T23:59:59.000Z

233

Training a Spiking Neural Network to Control a 4-DoF Robotic Arm based on Spike Timing-Dependent Plasticity  

E-Print Network [OSTI]

Training a Spiking Neural Network to Control a 4-DoF Robotic Arm based on Spike Timing network architecture that autonomously learns to control a 4 degree-of- freedom robotic arm after of the arm of an iCub humanoid robot. I. INTRODUCTION IN this work, we present a neural network architecture

Shanahan, Murray

234

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 6, NOVEMBER 1999 1305 On the Implementation of Frontier-to-Root Tree  

E-Print Network [OSTI]

of Frontier-to-Root Tree Automata in Recursive Neural Networks Marco Gori, Senior Member, IEEE, Andreas K network implementations of frontier-to-root tree automata (FRA). Specifically, we show that an FRAO (Mealy complexity of frontier-to- root tree automata (FRAO) implementations into recursive neural networks. Our work

Sperduti, Alessandro

235

Using neural networks to estimate redshift distributions. An application to CFHTLenS  

E-Print Network [OSTI]

We present a novel way of using neural networks (NN) to estimate the redshift distribution of a galaxy sample. We are able to obtain a probability density function (PDF) for each galaxy using a classification neural network. The method is applied to 58714 galaxies in CFHTLenS that have spectroscopic redshifts from DEEP2, VVDS and VIPERS. Using this data we show that the stacked PDF's give an excellent representation of the true $N(z)$ using information from 5, 4 or 3 photometric bands. We show that the fractional error due to using N(z_(phot)) instead of N(z_(truth)) is IPython notebook accompanying this paper is made available here: https://bitbucke...

Bonnett, Christopher

2013-01-01T23:59:59.000Z

236

Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier  

E-Print Network [OSTI]

AbstractóIn this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (?, ?, ?, ? and ?) and the Parsevalís theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.

I. Omerhodzic; S. Avdakovic; A. Nuhanovic; K. Dizdarevic

237

Closed loop adaptive control of spectrum-producing step using neural networks  

DOE Patents [OSTI]

Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller.

Fu, Chi Yung (San Francisco, CA)

1998-01-01T23:59:59.000Z

238

Fast Prediction of HCCI Combustion with an Artificial Neural Network Linked to a Fluid Mechanics Code  

SciTech Connect (OSTI)

We have developed an artificial neural network (ANN) based combustion model and have integrated it into a fluid mechanics code (KIVA3V) to produce a new analysis tool (titled KIVA3V-ANN) that can yield accurate HCCI predictions at very low computational cost. The neural network predicts ignition delay as a function of operating parameters (temperature, pressure, equivalence ratio and residual gas fraction). KIVA3V-ANN keeps track of the time history of the ignition delay during the engine cycle to evaluate the ignition integral and predict ignition for each computational cell. After a cell ignites, chemistry becomes active, and a two-step chemical kinetic mechanism predicts composition and heat generation in the ignited cells. KIVA3V-ANN has been validated by comparison with isooctane HCCI experiments in two different engines. The neural network provides reasonable predictions for HCCI combustion and emissions that, although typically not as good as obtained with the more physically representative multi-zone model, are obtained at a much reduced computational cost. KIVA3V-ANN can perform reasonably accurate HCCI calculations while requiring only 10% more computational effort than a motored KIVA3V run. It is therefore considered a valuable tool for evaluation of engine maps or other performance analysis tasks requiring multiple individual runs.

Aceves, S M; Flowers, D L; Chen, J; Babaimopoulos, A

2006-08-29T23:59:59.000Z

239

Closed loop adaptive control of spectrum-producing step using neural networks  

DOE Patents [OSTI]

Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller. 7 figs.

Fu, C.Y.

1998-11-24T23:59:59.000Z

240

Stochastic mean field formulation of the dynamics of diluted neural networks  

E-Print Network [OSTI]

We consider pulse-coupled Leaky Integrate-and-Fire neural networks with randomly distributed synaptic couplings. This random dilution induces fluctuations in the evolution of the macroscopic variables and deterministic chaos at the microscopic level. Our main aim is to mimic the effect of the dilution as a noise source acting on the dynamics of a globally coupled non-chaotic system. Indeed, the evolution of a diluted neural network can be well approximated as a fully pulse coupled network, where each neuron is driven by a mean synaptic current plus additive noise. These terms represent the average and the fluctuations of the synaptic currents acting on the single neurons in the diluted system. The main microscopic and macroscopic dynamical features can be retrieved with this stochastic approximation. Furthermore, the microscopic stability of the diluted network can be also reproduced, as demonstrated from the almost coincidence of the measured Lyapunov exponents in the deterministic and stochastic cases for an ample range of system sizes. Our results strongly suggest that the fluctuations in the synaptic currents are responsible for the emergence of chaos in this class of pulse coupled networks.

D. Angulo-Garcia; A. Torcini

2014-09-26T23:59:59.000Z

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

Study of t anti-t production in tau jets channel at CDFII using neural networks  

SciTech Connect (OSTI)

CDF (Collider Detector at Fermilab) is a particle detector located at Fermi National Laboratories, near Chicago. it allows to study decay products of p{bar p} collisions at center-of-mass energy of 1.96 TeV. During its first period of data taking (RunI), CDF observed for the first time the top quark (1995). The current period of data taking (RunII) is devoted to precise measurements of top properties and to search for new physics. This thesis work is about the top decay channel named {tau} + jets. A t{bar t} pair decays in two W bosons and two b quarks. In a {tau} + jets event, one out of the two W decays into two jets of hadrons, while the other produces a {tau} lepton and a neutrino; the {tau} decays semileptonically in one or more charged and neutral pions while b quarks hadronize producing two jets of particles. Thus the final state of a {tau} + jets event has this specific signature: five jets, one {tau}-like, i.e. narrow and with low track multiplicity, two from b quarks, two from a W boson and a large amount of missing energy from two {tau} neutrinos. They search for this signal in 311 pb{sup -1} of data collected with TOP{_}MULTIJET trigger. They use neural networks to separate signal from background and on the selected sample they perform a t{bar t} production cross section measurement. The thesis is structured as follows: in Chapter 1 they outline the physics of top and {tau}, concentrating on their discovery, production mechanisms and current physics results involving them. Chapter 2 is devoted to the description of the experimental setup: the accelerator complex first and CDF detector then. The trigger system is described in Chapter 3, while Chapter 4 shows how particles are reconstructed exploiting information from different CDF subdetectors. With Chapter 5 they begin to present their analysis: we use a feed forward neural network based on a minimization algorithm developed in Trento University, called Reactive Taboo Search (RTS), especially designed to rapidly escape from local minima. Using this neural network, they explore two techniques to select t{bar t} {yields} {tau} + jets events, the first based on a single net, the second on two neural networks in cascade; both techniques are described in Chapter 6, together with the variables used as inputs for the nets. Finally, in Chapter 7 they present a method to measure cross section on the sample of events selected by neural networks.

Amerio, Silvia; /Trento U.

2005-12-01T23:59:59.000Z

242

746 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 44, NO. 6, DECEMBER 1997 Adaptive Neural Network Control of  

E-Print Network [OSTI]

for the control of robot manipulators [4]­[7]. In general, neural network control design is done in two steps or the so-called Cartesian space. To apply robot manipulators to a w Network Control of Robot Manipulators in Task Space Shuzhi S. Ge, Member, IEEE, C. C. Hang, Senior Member

Ge, Shuzhi Sam

243

Stabilizing and Robustifying the Learning Mechanisms of Artificial Neural Networks  

E-Print Network [OSTI]

nonlinear dynamics of the plant, existence of a considerable amount of observation noise, and the adverse networks Z .ANN is the lack of stabilizing forces, the existence of which prevents the unbounded growth structure, which is trained on-line or off-line, will perform under the existence of strong external

Efe, Mehmet √?nder

244

Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network  

SciTech Connect (OSTI)

Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to develop code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix.

Susmikanti, Mike, E-mail: mike@batan.go.id [Center for Development of Nuclear Informatics, National Nuclear Energy Agency, PUSPIPTEK, Tangerang (Indonesia); Sulistyo, Jos, E-mail: soj@batan.go.id [Center for Nuclear Facilities Engineering, National Nuclear Energy Agency, PUSPIPTEK, Tangerang (Indonesia)

2014-09-30T23:59:59.000Z

245

Neural network prediction of aluminum-lithium weld strengths from acoustic emission amplitude data  

SciTech Connect (OSTI)

Acoustic emission (AE) flaw growth activity was monitored in aluminum-lithium weld specimens from the onset of tensile loading to failure. Data on actual ultimate strengths together with AE data from the beginning of loading up to 25 percent of the expected ultimate strength were used to train a backpropagation neural network to predict ultimate strengths. Architecturally, the fully interconnected network consisted of an input layer for the AE amplitude data, a hidden layer to accommodate failure mechanism mapping, and an output layer for ultimate strength prediction. The trained network was then applied to the prediction of ultimate strengths in the remaining six specimens. The worst case prediction error was found to be +2.6 percent.

Hill, E.V.K. (Embry-Riddle Aeronautical Univ., Daytona Beach, FL (United States). Aerospace Engineering Dept.); Israel, P.L. (Lamar Univ., Beaumont, TX (United States). Computer Science Dept.); Knotts, G.L. (Acoustic Emissions Consultants, Madison, AL (United States))

1993-09-01T23:59:59.000Z

246

Use of neural networks in the capacitance imaging system. Technical note  

SciTech Connect (OSTI)

The US Department of Energy`s Morgantown Energy Technology Center (METC) has developed a capacitance imaging system (CIS) to support its fluidized-bed research programs. The CIS uses 400 electric displacement current measurements taken between combinations of pairs of 32 electrodes to obtain a measure of the fluidized-bed material density in the volume between the electrodes. The measurements are simultaneously made for three other sets of horizontally-oriented 32 electrodes with the four sets evenly spaced vertically. This report describes the development of a method of using the 400 current measurements per level as the input to a neural network to produce the 193-pixel density estimates defined for each level. A 417-neuron subnetwork using 4,047 weights is defined as the system used to determine a set of 32-pixel densities in one of the annular regions of the fluidized-bed cross section. The same subnetwork with different values of weights is used for the other five annular regions that cover the rest of the cross section. An averaging technique is used to determine the density of the small central region. The methods used to optimize the set of weights for each of the six subnetworks are described. The results of tests using calibration electric current data as inputs to the neural system showed that these density estimates have less error than three previously developed methods of converting current measurements into pixel density maps. A comparison of the density maps produced by the neural system and the alternate three methods using input fluidization data also indicates the superior performance of the neural network approach.

Fasching, G.E.; Loudin, W.J.; Paton, D.E.; Smith, N.S. Jr.

1993-10-01T23:59:59.000Z

247

Application of holographic neural networks for flue gas emissions prediction in the Burnaby incinerator  

SciTech Connect (OSTI)

This article describes the development of a parametric prediction system (PPS) for various emission species at the Burnaby incinerator. The continuous emissions monitoring system at the Burnaby incinerator is shared between three boilers and therefore actual results are only available 5 minutes out of every 15 minutes. The PPS was developed to fill in data for the 10 minutes when the Continuous Emission Monitor (CEM) is measuring the other boilers. It bases its prediction on the last few actual readings taken and parametrically predicts CO, SO2 and NOx. The Burnaby Incinerator is located in the commercial/industrial area of South Burnaby, British Columbia. It consists of three separate lines, each burning ten tonnes of garbage per hour and producing about three tonnes of steam for every tonne of garbage burned. The air pollution control system first cools the combustion products with water injection and then scrubs them with very fine hydrated lime. Carbon is added to the lime to enhance the scrubbing of the combustion products. The CEM monitors the levels of oxygen, carbon monoxide, nitrogen oxides, sulphur dioxide and opacity. In 1996, an expert system was installed on one of boilers at the Burnaby Incinerator plant to determine if it could improve the plant=s operations and reduce overall emission. As part of the expert system, the PPS was developed. Holographic Neural Technology (HNeT), developed by AND Corporation of Toronto, Ontario, is a novel neural network technology using complex numbers in its architecture. Compared to the traditional neural networks, HNeT has some significant advantage. It is more resilient against converging on local minima; is faster training and executing; less prone to over fitting; and, in most cases, has significantly lower error. Selection of independent variabs, training set preparation, testing neural nets and other related issue will be discussed.

Zheng, L.; Dockrill, P.; Clements, B. [Natural Resources Canada, Nepean, Ontario (Canada). CANMET Energy Technology Centre

1997-12-31T23:59:59.000Z

248

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

E-Print Network [OSTI]

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...

Vasilic, Slavko

2004-09-30T23:59:59.000Z

249

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

E-Print Network [OSTI]

Over-parameterisation,a major obstacle to the use of artificial neural networks in hydrology ? 693 Hydrology and Earth System Sciences, 7(5), 693706 (2003) © EGU Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology ? Eric Gaume and Raphael Gosset Ecole Nationale des

Paris-Sud XI, Université de

250

IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART C: APPLICATIONS AND REVIEWS, VOL. 33, NO. 2, MAY 2003 259 A Neural-Network Based Control Solution  

E-Print Network [OSTI]

solution. Index Terms--Air-fuel ratio control, automotive fuel injection, air pollution, neural network. 2, MAY 2003 259 A Neural-Network Based Control Solution to Air-Fuel Ratio Control for Automotive the design of accurate control sys- tems to keep the air-to-fuel ratio at the optimal stoichiometric value AF

Alippi, Cesare

251

M. A. Sartori and P. J. Antsaklis, "Neural Network Implementations for Control Scheduling," Technical Report #91-04-02, Dept. of Electrical Engineering, University of Notre Dame, April 1991.  

E-Print Network [OSTI]

M. A. Sartori and P. J. Antsaklis, "Neural Network Implementations for Control Scheduling. Sartori and P. J. Antsaklis, "Neural Network Implementations for Control Scheduling," Technical Report #91. Antsaklis, "Neural Network Implementations for Control Scheduling," Technical Report #91-04-02, Dept

Antsaklis, Panos

252

On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications  

SciTech Connect (OSTI)

In this study we address the problem of predicting the position of a moving lung tumor during respiration on the basis of external breathing signals--a technique used for beam gating, tracking, and other dynamic motion management techniques in radiation therapy. We demonstrate the use of neural network filters to correlate tumor position with external surrogate markers while simultaneously predicting the motion ahead in time, for situations in which neither the breathing pattern nor the correlation between moving anatomical elements is constant in time. One pancreatic cancer patient and two lung cancer patients with mid/upper lobe tumors were fluoroscopically imaged to observe tumor motion synchronously with the movement of external chest markers during free breathing. The external marker position was provided as input to a feed-forward neural network that correlated the marker and tumor movement to predict the tumor position up to 800 ms in advance. The predicted tumor position was compared to its observed position to establish the accuracy with which the filter could dynamically track tumor motion under nonstationary conditions. These results were compared to simplified linear versions of the filter. The two lung cancer patients exhibited complex respiratory behavior in which the correlation between surrogate marker and tumor position changed with each cycle of breathing. By automatically and continuously adjusting its parameters to the observations, the neural network achieved better tracking accuracy than the fixed and adaptive linear filters. Variability and instability in human respiration complicate the task of predicting tumor position from surrogate breathing signals. Our results show that adaptive signal-processing filters can provide more accurate tumor position estimates than simpler stationary filters when presented with nonstationary breathing motion.

Isaksson, Marcus; Jalden, Joakim; Murphy, Martin J. [Department of Electrical Engineering, Stanford University, Stanford, California 94036 (United States); Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298 (United States)

2005-12-15T23:59:59.000Z

253

Comprehensive functional testing and dynamic compensation techniques for Cellular Neural Networks  

E-Print Network [OSTI]

is an analog computer that has the capability to solve a first order differential equation as is often required for spatial filtering operations in image processing applications. Each cell in the array is identical and is connected only to it's nearest... The first order nonlinear differential equation defining the dynamics of a cellular neural network cell can be written as follows [I]: C ' = ? ' + g A(i j;)r, l)y& &(r)+ g B(i j;k, l)u&&+I (la) [C()r, l)e)v(i j ) ' C(k, l)aV(i j ) y' j(r) (~xij (r)+l~ )ij...

Grimaila, Michael Russell

1995-01-01T23:59:59.000Z

254

HIERtalker: A default hierarchy of high order neural networks that learns to read English aloud  

SciTech Connect (OSTI)

A new learning algorithm based on a default hierarchy of high order neural networks has been developed that is able to generalize as well as handle exceptions. It learns the ''building blocks'' or clusters of symbols in a stream that appear repeatedly and convey certain messages. The default hierarchy prevents a combinatoric explosion of rules. A simulator of such a hierarchy, HIERtalker, has been applied to the conversion of English words to phonemes. Achieved accuracy is 99% for trained words and ranges from 76% to 96% for sets of new words. 8 refs., 4 figs., 1 tab.

An, Z.G.; Mniszewski, S.M.; Lee, Y.C.; Papcun, G.; Doolen, G.D.

1988-01-01T23:59:59.000Z

255

Comparative evaluation of neural-network-based and PI current controllers for HVDC transmission  

SciTech Connect (OSTI)

An investigation into a neural network (NN)-based controller, composed of a NN trained off-line in parallel with a NN trained on-line, is described in this paper. This NN controller has the potential of replacing the PI controller traditionally used for HVDC transmission systems. A theoretical basis for the operational behavior of the individual NN controllers is presented. Comparisons between the responses obtained with the NN and PI controllers for the rectifier of an HVDC transmission system are made under typical system perturbations and faults.

Sood, V.K.; Kandil, N.; Patel, R.V.; Khorasani, K. (Concordia Univ., Montreal, Quebec (Canada). Dept. of Electrical and Computer Engineering)

1994-05-01T23:59:59.000Z

256

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

E-Print Network [OSTI]

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

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

2015-03-03T23:59:59.000Z

257

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

258

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

259

Wireless Indoor Location Estimation Based on Neural Network RSS Signature Recognition (LENSR)  

SciTech Connect (OSTI)

Location Based Services (LBS), context aware applications, and people and object tracking depend on the ability to locate mobile devices, also known as localization, in the wireless landscape. Localization enables a diverse set of applications that include, but are not limited to, vehicle guidance in an industrial environment, security monitoring, self-guided tours, personalized communications services, resource tracking, mobile commerce services, guiding emergency workers during fire emergencies, habitat monitoring, environmental surveillance, and receiving alerts. This paper presents a new neural network approach (LENSR) based on a competitive topological Counter Propagation Network (CPN) with k-nearest neighborhood vector mapping, for indoor location estimation based on received signal strength. The advantage of this approach is both speed and accuracy. The tested accuracy of the algorithm was 90.6% within 1 meter and 96.4% within 1.5 meters. Several approaches for location estimation using WLAN technology were reviewed for comparison of results.

Kurt Derr; Milos Manic

2008-06-01T23:59:59.000Z

260

Exact computation of the Maximum Entropy Potential of spiking neural networks models  

E-Print Network [OSTI]

Understanding how stimuli and synaptic connectivity in uence the statistics of spike patterns in neural networks is a central question in computational neuroscience. Maximum Entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. But, in spite of good performance in terms of prediction, the ?tting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuro-mimetic models) provide a probabilistic mapping between stimulus, network architecture and spike patterns in terms of conditional proba- bilities. In this paper we build an exact analytical mapping between neuro-mimetic and Maximum Entropy models.

Cofre, Rodrigo

2014-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
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to obtain the most current and comprehensive results.


261

Using artificial neural networks to predict the quality and performance of oilfield cements  

SciTech Connect (OSTI)

Inherent batch to batch variability, ageing and contamination are major factors contributing to variability in oilfield cement slurry performance. Of particular concern are problems encountered when a slurry is formulated with one cement sample and used with a batch having different properties. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. We describe methods which allow the identification, characterization and prediction of the variability of oilfield cements. Our approach involves predicting cement compositions, particle size distributions and thickening time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders. Predictions make use of artificial neural networks. Slurry formulation thickening times can be predicted with uncertainties of less than {+-}10%. Composition and particle size distributions can be predicted with uncertainties a little greater than measurement error but general trends and differences between cements can be determined reliably. Our research shows that many key cement properties are captured within the Fourier transform infrared spectra of cement powders and can be predicted from these spectra using suitable neural network techniques. Several case studies are given to emphasize the use of these techniques which provide the basis for a valuable quality control tool now finding commercial use in the oilfield.

Coveney, P.V.; Hughes, T.L. [Schlumberger Cambridge Research Ltd., Cambridge (United Kingdom); Fletcher, P. [Schlumberger Dowell, Skene, Aberdeen (United Kingdom)

1996-12-31T23:59:59.000Z

262

170 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 45, NO. 1, FEBRUARY 1998 Application of Functional Link Neural Network to  

E-Print Network [OSTI]

for direct digital control (DDC) high- lights the recent trend toward more effective and efficient HVAC of Functional Link Neural Network to HVAC Thermal Dynamic System Identification Jason Teeter and Mo-Yuen Chow highlights the recent trend toward more effective and efficient heating, ventilating, and air- conditioning

Chow, Mo-Yuen

263

IEEE TRANSACTIONS ON NEURAL NETWORKS. VOL. 6. NO. 1. JANUARY 1995 51 Fuzzy Multi-Layer Perceptron,  

E-Print Network [OSTI]

system model, based on a fuzzy version of the multilayer perceptron developed by the authors, is proposedIEEE TRANSACTIONS ON NEURAL NETWORKS. VOL. 6. NO. 1. JANUARY 1995 51 Fuzzy Multi-Layer Perceptron. It infers the output class membership value(s) of an input pattern and also generates a measure of certainty

Mitra, Sushmita

264

Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding  

E-Print Network [OSTI]

for Protein Folding Richard Maclin Jude W. Shavlik Computer Sciences Dept. University of Wisconsin 1210 W learning Theory refinement Neural networks Finite-state automata Protein folding Chou-Fasman algorithm-Fasman algorithm, a method for predicting how globular proteins fold. Empirical evidence shows

Maclin, Rich

265

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

266

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 50, NO. 1, JANUARY 2001 125 The Application of Neural Networks to Fuel  

E-Print Network [OSTI]

of Neural Networks to Fuel Processors for Fuel-Cell Vehicles Laura C. Iwan and Robert F. Stengel, Fellow, IEEE Abstract--Passenger vehicles fueled by hydrocarbons or alco- hols and powered by proton exchange membrane (PEM) fuel cells address world air quality and fuel supply concerns while avoiding hydrogen

Stengel, Robert F.

267

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 3, MAY 2006 771 On Algorithmic Rate-Coded AER Generation  

E-Print Network [OSTI]

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 3, MAY 2006 771 On Algorithmic Rate-Coded AER of frames into the spike event-based representation known as the address-event-rep- resentation (AER). In this paper we concentrate on rate-coded AER. The problem is addressed as an algorithmic problem, in which

Barranco, Bernabe Linares

268

E-Print Network 3.0 - affects neural crest Sample Search Results  

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

induction was discovered in 1924 Summary: identified a gene that when overexpressed expanded the neural plate at the expense of adjacent neural crest... ). This was...

269

E-Print Network 3.0 - auditory neural activity Sample Search...  

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

percept 14,15. Neural computations for facilitating visual-auditory... this neural circuit is performing is also unclear. The ... Source: Groh, Jennifer. M. - Departments of...

270

E-Print Network 3.0 - applying bayesian neural Sample Search...  

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

Centre de mathmatiques Collection: Mathematics 90 Predicting EMG Activity from Neural Firing in M1 with Bayesian Backfitting Summary: Predicting EMG Activity from Neural Firing...

271

Artificial neural networks: Principle and application to model based control of drying systems -- A review  

SciTech Connect (OSTI)

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.

Thyagarajan, T.; Ponnavaikko, M. [Crescent Engineering Coll., Madras (India); Shanmugam, J. [Madras Inst. of Tech. (India); Panda, R.C.; Rao, P.G. [Central Leather Research Inst., Madras (India)

1998-07-01T23:59:59.000Z

272

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres  

SciTech Connect (OSTI)

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of {sup 252}Cf, {sup 241}AmBe and {sup 239}PuBe neutron sources measured with a Bonner spheres system.

Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac (Mexico); Vega-Carrillo, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac., Mexico. and Unidad Academica de Estudios Nucleares. C. Cip (Mexico)

2013-07-03T23:59:59.000Z

273

Discrimination Analysis of Earthquakes and Man-Made Events Using ARMA Coefficients Determination by Artificial Neural Networks  

SciTech Connect (OSTI)

A Quadratic Neural Networks (QNNs) model has been developed for identifying seismic source classification problem at regional distances using ARMA coefficients determination by Artificial Neural Networks (ANNs). We have devised a supervised neural system to discriminate between earthquakes and chemical explosions with filter coefficients obtained by windowed P-wave phase spectra (15 s). First, we preprocess the recording's signals to cancel out instrumental and attenuation site effects and obtain a compact representation of seismic records. Second, we use a QNNs system to obtain ARMA coefficients for feature extraction in the discrimination problem. The derived coefficients are then applied to the neural system to train and classification. In this study, we explore the possibility of using single station three-component (3C) covariance matrix traces from a priori-known explosion sites (learning) for automatically recognizing subsequent explosions from the same site. The results have shown that this feature extraction gives the best classifier for seismic signals and performs significantly better than other classification methods. The events have been tested, which include 36 chemical explosions at the Semipalatinsk test site in Kazakhstan and 61 earthquakes (mb = 5.0-6.5) recorded by the Iranian National Seismic Network (INSN). The 100% correct decisions were obtained between site explosions and some of non-site events. The above approach to event discrimination is very flexible as we can combine several 3C stations.

AllamehZadeh, Mostafa, E-mail: dibaparima@yahoo.com [International Institute of Earthquake Engineering and Seismology (Iran, Islamic Republic of)

2011-12-15T23:59:59.000Z

274

Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks  

SciTech Connect (OSTI)

In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural net approach it is possible to reduce the rate counts used to unfold the neutron spectrum. To evaluate these codes a computer tool called Neutron Spectrometry and dosimetry computer tool was designed. The results obtained with this package are showed. The codes here mentioned are freely available upon request to the authors.

Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac (Mexico); Vega-Carrillo, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac., Mexico. and Unidad Academica de Estudios Nucleares. C. Cip (Mexico)

2013-07-03T23:59:59.000Z

275

Determination of elastic properties of a film-substrate system by using the neural networks  

SciTech Connect (OSTI)

An inverse method based on artificial neural network (ANN) is presented to determine the elastic properties of films from laser-genrated surface waves. The surface displacement responses are used as the inputs for the ANN model; the outputs of the ANN are the Young's modulus, density, Poisson's ratio, and thickness of the film. The finite element method is used to calculate the surface displacement responses in a film-substrate system. Levenberg Marquardt algorithm is used as numerical optimization to speed up the training process for the ANN model. In this method, the materials parameters are not recovered from the dispersion curves but rather directly from the transient surface displacement. We have also found that this procedure is very efficient for determining the materials parameters of layered systems.

Xu Baiqiang; Shen Zhonghua; Ni Xiaowu; Wang Jijun; Guan Jianfei; Lu Jian [Department of Applied Physics, Nanjing University of Science and Technology, Nanjing 210094 (China); Faculty of Science, Jiangsu University, Zhenjiang 212013 (China); Department of Applied Physics, Nanjing University of Science and Technology, Nanjing 210094 (China); Department of Applied Physics, Nanjing University of Science and Technology, Nanjing 210094 (China); Faculty of Science, Jiangsu University, Zhenjiang 212013 (China); Department of Applied Physics, Nanjing University of Science and Technology, Nanjing 210094 (China)

2004-12-20T23:59:59.000Z

276

Complex dynamics of a delayed discrete neural network of two nonidentical neurons  

SciTech Connect (OSTI)

In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zou [J. Nonlinear Sci. 15, 291Ė303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415Ė432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869Ė1878 (2013)]. We also give some numeric simulations to verify our theoretical results.

Chen, Yuanlong [Mathematics Department, GuangDong University of Finance, Guangzhou 510521 (China)] [Mathematics Department, GuangDong University of Finance, Guangzhou 510521 (China); Huang, Tingwen [Mathematics Department, Texas A and M University at Qatar, P. O. Box 23874, Doha (Qatar)] [Mathematics Department, Texas A and M University at Qatar, P. O. Box 23874, Doha (Qatar); Huang, Yu, E-mail: stshyu@mail.sysu.edu.cn [Mathematics Department, Sun Yat-Sen University, Guangzhou 510275, People's Republic China (China)] [Mathematics Department, Sun Yat-Sen University, Guangzhou 510275, People's Republic China (China)

2014-03-15T23:59:59.000Z

277

A Coverage Study of the CMSSM Based on ATLAS Sensitivity Using Fast Neural Networks Techniques  

E-Print Network [OSTI]

We assess the coverage properties of confidence and credible intervals on the CMSSM parameter space inferred from a Bayesian posterior and the profile likelihood based on an ATLAS sensitivity study. In order to make those calculations feasible, we introduce a new method based on neural networks to approximate the mappin between CMSSM parameters and weak-scale particle masses. Our method reduces the computational effort needed to sample the CMSSM parameter space by a factor of ~10^4 with respect to conventional techniques. We find that both the Bayesian posterior and the profile likelihood intervals can significantly over-cover and trace back the origin of this effect to physical boundaries in the parameter space and overly-simplified likelihood functions.

Bridges, M; Feroz, F; Hobson, M; de Austri, R Ruiz; Trotta, R

2010-01-01T23:59:59.000Z

278

Artificial Neural Networks and Support Vector Machines for Water Demand Time Series Forecasting  

E-Print Network [OSTI]

Water plays a pivotal role in many physical processes, and most importantly in sustaining human life, animal life and plant life. Water supply entities therefore have the responsibility to supply clean and safe water at the rate required by the consumer. It is therefore necessary to implement mechanisms and systems that can be employed to predict both short-term and long-term water demands. The increasingly growing field of computational intelligence techniques has been proposed as an efficient tool in the modelling of dynamic phenomena. The primary objective of this paper is to compare the efficiency of two computational intelligence techniques in water demand forecasting. The techniques under comparison are the Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs). In this study it was observed that the ANNs perform better than the SVMs. This performance is measured against the generalisation ability of the two.

Msiza, Ishmael S; Nelwamondo, Fulufhelo Vincent

2007-01-01T23:59:59.000Z

279

Technical and analytical support to the ARPA Artificial Neural Network Technology Program  

SciTech Connect (OSTI)

Strategic Analysis (SA) has provided ongoing work for the Advanced Research Projects Agency (ARPA) Artificial Neural Network (ANN) technology program. This effort provides technical and analytical support to the ARPA ANN technology program in support of the following information areas of interest: (1) Alternative approaches for application of ANN technology, hardware approaches that utilize the inherent massive parallelism of ANN technology, and novel ANN theory and modeling analyses. (2) Promising military applications for ANN technology. (3) Measures to use in judging success of ANN technology research and development. (4) Alternative strategies for ARPA involvement in ANN technology R&D. These objectives were accomplished through the development of novel information management tools, strong SA knowledge base, and effective communication with contractors, agents, and other program participants. These goals have been realized. Through enhanced tracking and coordination of research, the ANN program is healthy and recharged for future technological breakthroughs.

NONE

1995-09-16T23:59:59.000Z

280

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

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

Novel Technique of Sizing the Stand-Alone Photovoltaic Systems Using the Radial Basis Function Neural Networks: Application in Isolated Sites  

E-Print Network [OSTI]

The objective of this work is to investigate the Radial Basis Function Neural Networks (RBFN) to identifying and modeling the optimal sizing couples of stand-alone photovoltaic (PV) system using a minimum of input data, These optimal couples allow...

Mellit, A.; Benghanme, M.; Arab, A. H.; Guessoum, A.

2004-01-01T23:59:59.000Z

282

Automated Classification of Stellar Spectra. II: Two-Dimensional Classification with Neural Networks and Principal Components Analysis  

E-Print Network [OSTI]

We investigate the application of neural networks to the automation of MK spectral classification. The data set for this project consists of a set of over 5000 optical (3800-5200 AA) spectra obtained from objective prism plates from the Michigan Spectral Survey. These spectra, along with their two-dimensional MK classifications listed in the Michigan Henry Draper Catalogue, were used to develop supervised neural network classifiers. We show that neural networks can give accurate spectral type classifications (sig_68 = 0.82 subtypes, sig_rms = 1.09 subtypes) across the full range of spectral types present in the data set (B2-M7). We show also that the networks yield correct luminosity classes for over 95% of both dwarfs and giants with a high degree of confidence. Stellar spectra generally contain a large amount of redundant information. We investigate the application of Principal Components Analysis (PCA) to the optimal compression of spectra. We show that PCA can compress the spectra by a factor of over 30 while retaining essentially all of the useful information in the data set. Furthermore, it is shown that this compression optimally removes noise and can be used to identify unusual spectra.

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

1998-03-05T23:59:59.000Z

283

E-Print Network 3.0 - adult neural stem Sample Search Results  

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

Summary: of adult human stem cells from the adult neural retina, as well as the standardization of methods... cell lines derived from adult human retina exhibit neural stem cell...

284

E-Print Network 3.0 - adult human neural Sample Search Results  

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

Summary: of adult human stem cells from the adult neural retina, as well as the standardization of methods... cell lines derived from adult human retina exhibit neural stem cell...

285

Hybrid Neural Systems Stefan Wermter  

E-Print Network [OSTI]

Hybrid Neural Systems Stefan Wermter Ron Sun Springer, Heidelberg, New York January 2000 #12; Preface The aim of this book is to present a broad spectrum of current research in hybrid neural systems, and advance the state of the art in neural networks and arti#12;cial intelligence. Hybrid neural systems

Varela, Carlos

286

Forecasting of preprocessed daily solar radiation time series using neural networks  

SciTech Connect (OSTI)

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

287

An adaptive neural network approach to one-week ahead load forecasting  

SciTech Connect (OSTI)

A new neural network approach is applied to one-week ahead load forecasting. This approach uses a linear adaptive neuron or adaptive linear combiner called Adaline.'' An energy spectrum is used to analyze the periodic components in a load sequence. The load sequence mainly consists of three components: base load component, and low and high frequency load components. Each load component has a unique frequency range. Load decomposition is made for the load sequence using digital filters with different passband frequencies. After load decomposition, each load component can be forecasted by an Adaline. Each Adaline has an input sequence, an output sequence, and a desired response-signal sequence. It also has a set of adjustable parameters called the weight vector. In load forecasting, the weight vector is designed to make the output sequence, the forecasted load, follow the actual load sequence; it also has a minimized Least Mean Square error. This approach is useful in forecasting unit scheduling commitments. Mean absolute percentage errors of less than 3.4 percent are derived from five months of utility data, thus demonstrating the high degree of accuracy that can be obtained without dependence on weather forecasts.

Peng, T.M. (Pacific Gas and Electric Co., San Francisco, CA (United States)); Hubele, N.F.; Karady, G.G. (Arizona State Univ., Tempe, AZ (United States))

1993-08-01T23:59:59.000Z

288

Neural network technology as a pollution prevention tool in the electric utility industry  

SciTech Connect (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 US 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 calculations indicate that a 1% improvement in thermal efficiency at the 430 MW gas-fired utility boiler could results in an estimated energy savings of 142, 140 mmBtus and carbon dioxide (CO{sub 2}) reductions of 8,774 tons per year. This paper describes the process 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.

1998-07-01T23:59:59.000Z

289

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

290

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

291

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

292

E-Print Network 3.0 - actions neural evidence Sample Search Results  

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

of Psychology, University of Pennsylvania Summary: ). An "as soon as possible" effect in human inter- temporal decision making: Behavioral evidence and neural... ., and...

293

Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network  

E-Print Network [OSTI]

R. Satake, Prediction of energy demands using neural networkof Building Heating Energy Demand Using Artificial Neuralknow energy flows and energy demand of the buildings for the

Paudel, Subodh; Elmtiri, Mohamed; Kling, Wil L; Corre, Olivier Le; Lacarriere, Bruno

2014-01-01T23:59:59.000Z

294

E-Print Network 3.0 - auditory neural maturation Sample Search...  

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

Speech-induced suppression of evoked auditory fields in children who stutter Deryk S. Beal a, Summary: that the neural correlates of auditory feedback processing of...

295

ESANN'1997 proceedings -European Symposium on Artificial Neural Networks Bruges (Belgium), 16-17-18 April 1997, D-Facto public., ISBN 2-9600049-7-3, pp. 151-156  

E-Print Network [OSTI]

ESANN'1997 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 16-17-18 April 1997, D-Facto public., ISBN 2-9600049-7-3, pp. 151-156 #12;ESANN'1997 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 16-17-18 April 1997, D-Facto public., ISBN 2

Avignon et des Pays de Vaucluse, Université de

296

On the neutron noise diagnostics of pressurized water reactor control rod vibrations. 4: Application of neural networks  

SciTech Connect (OSTI)

A neutron noise-based technique for the localization of excessively vibrating control rods is elaborated upon in the previous three papers of this series. The method is based on the inversion of a formula that expresses the auto- and cross spectra of three neutron detector signals through the parameters of the vibrating rod, i.e., equilibrium position and displacement components. Successful tests of the algorithm with both simulated and real data were reported in the previous papers. The algorithm had nevertheless certain drawbacks, namely, that its use requires expert knowledge, the redundancy of extra detectors cannot be utilized, and with realistic transfer functions the calculations are rather lengthy. The use of neural networks offers an alternative way of performing the inversion procedure. This possibility was investigated by constructing a network that was trained to determine the rod position from the detector spectra. It was found that all shortcomings of the traditional localization method can be eliminated. The neural network-based identification was also tested with success.

Pazsit, I.; Garis, N.S. [Chalmers Univ. of Technology, Goeteborg (Sweden). Dept. of Reactor Physics; Gloeckler, O. [Ontario Hydro Nuclear, Toronto, Ontario (Canada)

1996-09-01T23:59:59.000Z

297

Neural networks and separation of Cosmic Microwave Background and astrophysical signals in 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 test 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 {\\sc Planck} Surveyor Satellite. The maps are at the frequencies of the Low Frequency Instrument (LFI) aboard the {\\sc 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 signal, with precision going from 10% for the Galactic components to percent for CMB; radio sources are almost completely recovered down to a flux limit corresponding to $0.7\\sigma_{CMB}$, where $\\sigma_{CMB}$ is the rms level of CMB fluctuations. The signal recovering possesses equal quality on all the scales larger then the pixel size. In addition, we show that the frequency scalings of the input signals can be partially inferred from the ICA outputs, at the percent precision for the dominant components, radio sources and CMB.

C. Baccigalupi; L. Bedini; C. Burigana; G. De Zotti; A. Farusi; D. Maino; M. Maris; F. Perrotta; E. Salerno; L. Toffolatti; A. Tonazzini

2000-06-21T23:59:59.000Z

298

PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 1, Theory  

SciTech Connect (OSTI)

The function of the PRODIAG code is to diagnose on-line the root cause of a thermal-hydraulic (T-H) system transient with trace back to the identification of the malfunctioning component using the T-H instrumentation signals exclusively. The code methodology is based on the Al techniques of automated reasoning/expert systems (ES) and artificial neural networks (ANN). The research and development objective is to develop a generic code methodology which would be plant- and T-H-system-independent. For the ES part the only plant or T-H system specific code requirements would be implemented through input only and at that only through a Piping and Instrumentation Diagram (PID) database. For the ANN part the only plant or T-H system specific code requirements would be through the ANN training data for normal component characteristics and the same PID database information. PRODIAG would, therefore, be generic and portable from T-H system to T-H system and from plant to plant without requiring any code-related modifications except for the PID database and the ANN training with the normal component characteristics. This would give PRODIAG the generic feature which numerical simulation plant codes such as TRAC or RELAP5 have. As the code is applied to different plants and different T-H systems, only the connectivity information, the operating conditions and the normal component characteristics are changed, and the changes are made entirely through input. Verification and validation of PRODIAG would, be T-H system independent and would be performed only ``once``.

Reifman, J.; Wei, T.Y.C.; Vitela, J.E.

1995-09-01T23:59:59.000Z

299

The use of artificial neural networks in PVT-based radiation portal monitors  

SciTech Connect (OSTI)

Polyvinyl toluene (PVT) based gamma-ray scintillation detectors are cost effective for use in radiation portal monitors (RPMs) applied to screening for illicit radioactive materials at international border crossings. While PVT detectors provide good sensitivity in detecting the presence of radioactive materials, they provide poor spectral resolution, limiting their ability to identify the isotopic content of the source of radiation. Thus using only total-spectrum or gross-count alarm algorithms, PVT-based RPMs cannot distinguish innocent materials that contain low-levels of normally occurring radioactivity from special nuclear materials of concern. To reduce the number of ďnuisanceĒ alarms produced in PVT-based RPMs by innocent materials, algorithms that analyze spectra from PVT detectors must be optimized to make use of the limited information contained in their energy spectra. This paper discusses how artificial neural networks (ANNs) can be used in such an analysis. The objective was to reduce the nuisance/false alarm probability while maintaining high detection probabilities, thus allowing gross count alarm thresholds to be raised without loss of performance and sensitivity to radioactive materials of interest. The spectra used in this study were obtained from actual PVT-based RPM data, and included cases where simulated spectra were inserted into the measured spectra. This paper also includes an analysis of spectral channel importance and shows evaluations of two methods used to rebin energy spectra into smaller sets. The results show that ANNs can be used with RPMs to reduce nuisance alarms. The algorithms described can be used in analyzing PVT spectra, and potentially sodium iodide spectra.

Kangas, Lars J.; Keller, Paul E.; Siciliano, Edward R.; Kouzes, Richard T.; Ely, James H.

2008-03-21T23:59:59.000Z

300

Three-Dimensional Spectral Classification of Low-Metallicity Stars Using Artificial Neural Networks  

E-Print Network [OSTI]

We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (Teff, logg, and [Fe/H]) for Galactic F- and G-type stars. The ANNs are fed with medium-resolution (~ 1-2 A) non flux-calibrated spectroscopic observations. From a sample of 279 stars with previous high-resolution determinations of metallicity, and a set of (external) estimates of temperature and surface gravity, our ANNs are able to predict Teff with an accuracy of ~ 135-150 K over the range 4250 <= Teff <= 6500 K, logg with an accuracy of ~ 0.25-0.30 dex over the range 1.0 <= logg <= 5.0 dex, and [Fe/H] with an accuracy ~ 0.15-0.20 dex over the range -4.0 <= [Fe/H] <= +0.3. Such accuracies are competitive with the results obtained by fine analysis of high-resolution spectra. It is noteworthy that the ANNs are able to obtain these results without consideration of photometric information for these stars. We have also explored the impact of the signal-to-noise ratio (S/N) on the behavior of ANNs, and conclude that, when analyzed with ANNs trained on spectra of commensurate S/N, it is possible to extract physical parameter estimates of similar accuracy with stellar spectra having S/N as low as 13. Taken together, these results indicate that the ANN approach should be of primary importance for use in present and future large-scale spectroscopic surveys.

Shawn Snider; Carlos Allende Prieto; Ted von Hippel; Timothy C. Beers; Christopher Sneden; Yuan Qu; and Silvia Rossi

2001-07-20T23:59:59.000Z

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 landscape approach to reserving farm ponds for wintering bird refuges in Taoyuan, Taiwan  

E-Print Network [OSTI]

regression with error back-propagation into the paradigm of artificial neural networks (ANN). The model considers pond shape, size, neighboring farmlands, and developed areas in calculating parameters pertaining to their respective and interactive influences...

Fang, Wei-Ta

2006-08-16T23:59:59.000Z

302

Quantum and quasi-classical dynamics of the OH + CO ? H + CO{sub 2} reaction on a new permutationally invariant neural network potential energy surface  

SciTech Connect (OSTI)

A permutationally invariant global potential energy surface for the HOCO system is reported by fitting a larger number of high-level ab initio points using the newly proposed permutation invariant polynomial-neural network method. The small fitting error (?5 meV) indicates a faithful representation of the potential energy surface over a large configuration space. Full-dimensional quantum and quasi-classical trajectory studies of the title reaction were performed on this potential energy surface. While the results suggest that the differences between this and an earlier neural network fits are small, discrepancies with state-to-state experimental data remain significant.

Li, Jun; Guo, Hua, E-mail: zhangdh@dicp.ac.cn, E-mail: hguo@unm.edu [Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States)] [Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States); Chen, Jun; Zhang, Dong H., E-mail: zhangdh@dicp.ac.cn, E-mail: hguo@unm.edu [State key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian 116023 (China)

2014-01-28T23:59:59.000Z

303

E-Print Network 3.0 - anger expression neural Sample Search Results  

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

.7 notes how the model may inform study of the neural substrates of anger. 12.2 Phenomenology... and Importance of Childhood Anger Episodes of anger are frequent in early...

304

INFORMATION ISSN 1343-4500 Computing an Incompressible Viscous Fluid Flow Using Neural Network Based  

E-Print Network [OSTI]

of the learning system are in good agreement with the available previous works. Key Words: learning system; neural or a plasticized solid material acted upon by forces causing it to change the shape. The popular methods

305

E-Print Network 3.0 - autonomic neural circuits Sample Search...  

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

MICRO-AIR-VEHICLE and William E. Green Summary: NEURAL NETS AND OPTIC FLOW FOR AUTONOMOUS MICRO-AIR-VEHICLE NAVIGATION Paul Y. Oh and William E... , autonomous collision avoidance...

306

Validating real-time implementations of diagonal recurrent neural network and fuzzy logic controllers for nuclear reactor control  

SciTech Connect (OSTI)

A diagonal recurrent neural network (DRNN) and a fuzzy logic controller (FLC) were designed for optimal temperature control. The DRNN and FLC utilize the response of a fifth-order, one delayed neutron group linear model to an optimal controller that was previously designed. In this paper, the results of testing a real-time implementation of the DRNN and FLC are presented. The method of testing that was utilized is called hardware in loop simulation (HILS). Here, a distributed simulation of a highly non-linear model of the reactor on a network of workstations is used in the final step of testing prior to experimentation on the reactor. Also, in this setup actual control hardware is used to validate the controller realizations.

Ramaswamy, P.; Edwards, R.M.; Lee, K.Y.

1994-12-31T23:59:59.000Z

307

A Unified Neural Network Model for the Self-organization of Topographic Receptive Fields and Lateral Interaction  

E-Print Network [OSTI]

A self-organizing neural network model for the simultaneous development of topographic receptive fields and lateral interactions in cortical maps is presented. Both afferent and lateral connections adapt by the same Hebbian mechanism in a purely local and unsupervised learning process. Afferent input weights of each neuron self-organize into hill-shaped profiles, receptive fields organize topographically across the network, and unique lateral interaction profiles develop for each neuron. The resulting self-organized structure remains in a dynamic and continuously-adapting equilibrium with the input. The model can be seen as a generalization of previous self-organizing models of the visual cortex, and provides a general computational framework for experiments on receptive field development and cortical plasticity. The model also serves to point out general limits on activity-dependent self-organization: when multiple inputs are presented simultaneously, the receptive field centers need ...

1995-01-01T23:59:59.000Z

308

Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses  

E-Print Network [OSTI]

We investigate the effect of electric synapses (gap junctions) on collective neuronal dynamics and spike statistics in a conductance-based Integrate-and-Fire neural network, driven by a Brownian noise, where conductances depend upon spike history. We compute explicitly the time evolution operator and show that, given the spike-history of the network and the membrane potentials at a given time, the further dynamical evolution can be written in a closed form. We show that spike train statistics is described by a Gibbs distribution whose potential can be approximated with an explicit formula, when the noise is weak. This potential form encompasses existing models for spike trains statistics analysis such as maximum entropy models or Generalized Linear Models (GLM). We also discuss the different types of correlations: those induced by a shared stimulus and those induced by neurons interactions.

Rodrigo Cofrť; Bruno Cessac

2012-12-14T23:59:59.000Z

309

Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines  

E-Print Network [OSTI]

In this work, the beta-decay halflives problem is dealt as a nonlinear optimization problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing past similar approaches, we have constructed sophisticated Artificial Neural Networks (ANNs) and Support Vector Regression Machines (SVMs) for each class with even-odd character in Z and N to global model the systematics of nuclei that decay 100% by the beta-minus-mode in their ground states. The arising large-scale lifetime calculations generated by both types of machines are discussed and compared with each other, with the available experimental data, with previous results obtained with neural networks, as well as with estimates coming from traditional global nuclear models. Particular attention is paid on the estimates for exotic and halo nuclei and we focus to those nuclides that are involved in the r-process nucleosynthesis. It is found that statistical models based on LM can at least match or even surpass the predictive performance of the best conventional models of beta-decay systematics and can complement the latter.

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

2008-09-02T23:59:59.000Z

310

An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks  

SciTech Connect (OSTI)

The Adaptive Landscape Classification Procedure (ALCP), which links the advanced geospatial analysis capabilities of Geographic Information Systems (GISs) and Artificial Neural Networks (ANNs) and particularly Self-Organizing Maps (SOMs), is proposed as a method for establishing and reducing complex data relationships. Its adaptive and evolutionary capability is evaluated for situations where varying types of data can be combined to address different prediction and/or management needs such as hydrologic response, water quality, aquatic habitat, groundwater recharge, land use, instrumentation placement, and forecast scenarios. The research presented here documents and presents favorable results of a procedure that aims to be a powerful and flexible spatial data classifier that fuses the strengths of geoinformatics and the intelligence of SOMs to provide data patterns and spatial information for environmental managers and researchers. This research shows how evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Certainly, environmental management and research within heterogeneous watersheds provide challenges for consistent evaluation and understanding of system functions. For instance, watersheds over a range of scales are likely to exhibit varying levels of diversity in their characteristics of climate, hydrology, physiography, ecology, and anthropogenic influence. Furthermore, it has become evident that understanding and analyzing these diverse systems can be difficult not only because of varying natural characteristics, but also because of the availability, quality, and variability of spatial and temporal data. Developments in geospatial technologies, however, are providing a wide range of relevant data, and in many cases, at a high temporal and spatial resolution. Such data resources can take the form of high-dimensional data arrays, which can difficult to fully use. Establishing relationships among high-dimensional datasets through neurocomputing based patterning methods can help 1) resolve large volumes of data into a meaningful form; 2) provide an approach for inferring landscape processes in areas that have limited data available but that exhibit similar landscape characteristics; and 3) discover the value of individual variables or groups of variables that contribute to specific processes in the landscape.

Coleman, Andre M.

2008-08-01T23:59:59.000Z

311

ELG7177 -Topics in Communications I: Neural Networks and Fuzzy Systems Winter 2009 (Jan. 6 -April 9): Tuesday, 11:30-13:00, CBY B202  

E-Print Network [OSTI]

enforcement. Applications: neuro-fuzzy modelling and control, pattern recognition. Course outline: 1 NN architectures. 7. Applications of NN NN modeling of 3D objects. Pattern recognition. InverseELG7177 - Topics in Communications I: Neural Networks and Fuzzy Systems Winter 2009 (Jan. 6 - April

Petriu, Emil M.

312

To appear in The International Journal of Intelligent Automation and Soft Computing, 2003 Detection of Welding Flaws with MLP Neural Network and Case Based Reasoning  

E-Print Network [OSTI]

Detection of Welding Flaws with MLP Neural Network and Case Based Reasoning T. Warren Liao1 *, E-Ze University, Nei-Li 32026, Chung-Li, Taiwan Abstract - The correct detection of welding flaws is important to the successful development of an automated weld inspection system. As a continuation of our previous efforts

Triantaphyllou, Evangelos

313

Abstract-This paper proposes a neural network based approach to estimating the maximum possible output power of a solar photovoltaic  

E-Print Network [OSTI]

on a shaded solar panel at different hours of a day for several days. After training the neural network, its, building-integrated photovoltaic panels, and portable solar tents, it is common for a solar PV to become output power of a solar photovoltaic array under the non-uniform shadow conditions at a given geographic

Lehman, Brad

314

arXiv:0905.3759v3[nlin.AO]27Jan2010 Evolving networks and the development of neural  

E-Print Network [OSTI]

arXiv:0905.3759v3[nlin.AO]27Jan2010 Evolving networks and the development of neural systems Samuel Johnson, J. Marro and Joaqu¬īin J. Torres Departamento de Electromagnetismo y F¬īisica de la Materia Granada, Spain. E-mail: samuel@onsager.ugr.es, jmarro@ugr.es and jtorres@onsager.ugr.es Abstract

Johnson, Samuel

315

Paper accepted for presentation at 2003 IEEE Bologna PowerTech Conference, June 23-26, Bologna, Italy Wind Power Forecasting using Fuzzy Neural Networks  

E-Print Network [OSTI]

, Italy Wind Power Forecasting using Fuzzy Neural Networks Enhanced with On-line Prediction Risk) as input, to predict the power production of wind park8 48 hours ahead. The prediction system integrates of the numerical weather predictions. Index Term-Wind power, short-term forecasting, numerical weather predictions

Paris-Sud XI, Universitť de

316

1196 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 7, JULY 2008 On Real-Time AER 2-D Convolutions Hardware for  

E-Print Network [OSTI]

1196 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 7, JULY 2008 On Real-Time AER 2-D­event-rep- resentation (AER) technique, which is a spike-based biologically inspired image and video representation interfaces have been developed for generating AER streams from conven- tional computers and feeding them

Barranco, Bernabe Linares

317

Reservoir characterization using seismic attributes, well data, and artificial neural networks  

E-Print Network [OSTI]

to select among numerous networks which one could be the most efficient for approximating a relationship between seismic attributes and reservoir parameters (porosity, shaliness, water saturation) derived from well log data. The selected network is trained...

Toinet, Sylvain

2012-06-07T23:59:59.000Z

318

Real time selective harmonic minimization for multilevel inverters using genetic algorithm and artifical neural network angle generation  

SciTech Connect (OSTI)

The work developed here proposes a methodology for calculating switching angles for varying DC sources in a multilevel cascaded H-bridges converter. In this approach the required fundamental is achieved, the lower harmonics are minimized, and the system can be implemented in real time with low memory requirements. Genetic algorithm (GA) is the stochastic search method to find the solution for the set of equations where the input voltages are the known variables and the switching angles are the unknown variables. With the dataset generated by GA, an artificial neural network (ANN) is trained to store the solutions without excessive memory storage requirements. This trained ANN then senses the voltage of each cell and produces the switching angles in order to regulate the fundamental at 120 V and eliminate or minimize the low order harmonics while operating in real time.

Filho, Faete J [ORNL; Tolbert, Leon M [ORNL; Ozpineci, Burak [ORNL

2012-01-01T23:59:59.000Z

319

Dynamics of neural cryptography  

SciTech Connect (OSTI)

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido [Institut fuer Theoretische Physik, Universitaet Wuerzburg, Am Hubland, 97074 Wuerzburg (Germany); Minerva Center and Department of Physics, Bar Ilan University, Ramat Gan 52900 (Israel)

2007-05-15T23:59:59.000Z

320

Neural network approaches to tracer identification as related to PIV research  

E-Print Network [OSTI]

code ivas capable of extracting, the centroids of all of the syntlie?c spots This network wns tlieu apl&lied to nn &uiage taken fro&n n previous expenment It perfornied as well as a global thresholdrng algor&thm The cellular network was the lenst... elec!inc H wever, tlie pronf given by !Bi?sky ivas nnly for single-laver per. eptrnns, and did nnt apply tc multi-layer network. Several researc!icis, :ca&ca& ivhoni were Ivohouen f'ros)bm" A?dere&?i, aud Hop- f&elcl, niauitaiued their enthusiasui f...

Seeley, Charles Henry

1992-01-01T23:59:59.000Z

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.


321

Adaptive Gain and Order Scheduling of Optimal Fractional Order PI{\\lambda}D{\\mu} Controllers with Radial Basis Function Neural-Network  

E-Print Network [OSTI]

Gain and order scheduling of fractional order (FO) PI{\\lambda}D{\\mu} controllers are studied in this paper considering four different classes of higher order processes. The mapping between the optimum PID/FOPID controller parameters and the reduced order process models are done using Radial Basis Function (RBF) type Artificial Neural Network (ANN). Simulation studies have been done to show the effectiveness of the RBFNN for online scheduling of such controllers with random change in set-point and process parameters.

Das, Saptarshi; Mukherjee, Ayan; Pan, Indranil; Gupta, Amitava; 10.1109/PACC.2011.5979047

2012-01-01T23:59:59.000Z

322

Two-way communication with neural networks in vivo using focused light  

E-Print Network [OSTI]

Neuronal networks process information in a distributed, spatially heterogeneous manner that transcends the layout of electrodes. In contrast, directed and steerable light offers the potential to engage specific cells on ...

Schummers, James

323

A Self-Organizing Neural Network for Contour Integration through Synchronized Firing  

E-Print Network [OSTI]

Contour integration in low-level vision is believed to occur based on lateral interaction between neurons with similar orientation tuning. The exact neural mechanisms underlying such interactions, and their developmental origins, are not well understood. This paper suggests through computational simulations that synchronized firing of neurons mediated by patchy lateral connections, formed through input-driven selforganization, can serve as such a mechanism. Furthermore, we argue that different degree of such patchy connections established during development may explain why different areas of the visual field show different degrees of contour integration in psychophysical experiments. Introduction Contour integration in low-level vision means forming a single coherent percept (i.e. a continuous contour) from a discontinuous sequence of line segments. Humans are very good at contour integration; understanding the underlying mechanisms can give us insights into how perceptual...

2000-01-01T23:59:59.000Z

324

Generic Emergence of Cognitive Behaviour in Self-Generating Neural Networks  

E-Print Network [OSTI]

the whole object the structure of a directed graph. 3. A matrix of connectivity (synaptic) strengths, which activities. Our design is implemented using Matlab, such that the growth process of the network and its (1987). What all these nets have in common is that their graph structure is fixed once and for all

Bohun, C. Sean

325

Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation  

E-Print Network [OSTI]

wall power (MJ) kk Estimation of partial autocorrelations for time lag k PV Plant efficiency (%) R Networks, PV Plant, Energy Prediction, Stationarity *Corresponding author: Marc,MUSELLI, tél: +33 4 95 52 S Surface of PV wall [m²] x Parameter to optimize PR Performance ratio of the PV plant J Jacobian matrix hal

Paris-Sud XI, Université de

326

Machine Learning Research Group Working Paper 912 Refining Algorithms with KnowledgeBased Neural Networks  

E-Print Network [OSTI]

Networks: Improving the Chou­Fasman Algorithm for Protein Folding * Richard Maclin Jude W. Shavlik Computer­Fasman algorithm, a method for predicting how globular proteins fold. The Chou­Fasman algorithm cannot be elegantly and discuss the need for better definitions of solution quality for the protein­folding problem. 1

Liblit, Ben

327

Identification of parameters influencing the response of gas storage wells to hydraulic fracturing with the aid of a neural network  

SciTech Connect (OSTI)

Performing hydraulic fractures on gas storage wells to improve their deliverability is a common practice in the eastern part of the US. Most fields used for storage in this region are old, and the reservoir characteristic data necessary for most reservoir studies and hydraulic fracture design and evaluation are scarce. This paper introduces a new method by which parameters that influence the response of gas storage wells to hydraulic fracturing may be identified in the absence of sufficient reservoir data. Control and manipulation of these parameters, once identified correctly, could enhance the outcome of frac jobs in gas storage fields. The authors conducted the study on a gas storage field in the Clinton formation of northeastern Ohio. They found that well-performance indicators before a hydraulic fracture play an important role in how good the well will respond to a new frac job. They also identified several other important factors. The identification of controlling parameters serves as a foundation for improved frac job design in the fields where adequate engineering data are not available. Another application of this type of study could be the enhancement of selection criteria among the candidate wells for hydraulic fracturing. To achieve the objective of this study, the authors designed, trained, and applied an artificial neural network. The paper will discuss the results of the incorporation of this new technology in hydraulic fracture design and evaluation.

McVey, D.S. [East Ohio Gas Co., North Canton, OH (United States); Mohaghegh, S.; Aminian, K.; Ameri, S. [West Virginia Univ., Morgantown, WV (United States)

1996-04-01T23:59:59.000Z

328

Fast Prediction of HCCI and PCCI Combustion with an Artificial Neural Network-Based Chemical Kinetic Model  

SciTech Connect (OSTI)

We have added the capability to look at in-cylinder fuel distributions using a previously developed ignition model within a fluid mechanics code (KIVA3V) that uses an artificial neural network (ANN) to predict ignition (The combined code: KIVA3V-ANN). KIVA3V-ANN was originally developed and validated for analysis of Homogeneous Charge Compression Ignition (HCCI) combustion, but it is also applicable to the more difficult problem of Premixed Charge Compression Ignition (PCCI) combustion. PCCI combustion refers to cases where combustion occurs as a nonmixing controlled, chemical kinetics dominated, autoignition process, where the fuel, air, and residual gas mixtures are not necessarily as homogeneous as in HCCI combustion. This paper analyzes the effects of introducing charge non-uniformity into a KIVA3V-ANN simulation. The results are compared to experimental results, as well as simulation results using a more physically representative and computationally intensive code (KIVA3V-MPI-MZ), which links a fluid mechanics code to a multi-zone detailed chemical kinetics solver. The results indicate that KIVA3V-ANN produces reasonable approximations to the more accurate KIVA3V-MPI-MZ at a much reduced computational cost.

Piggott, W T; Aceves, S M; Flowers, D L; Chen, J Y

2007-09-26T23:59:59.000Z

329

bib-neural | netl.doe.gov  

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

Big Bend Power Station Neural Network-Intelligent Sootblower (NN-ISB) Optimization - Project Brief PDF-154KB Tampa Electric Company, Apollo Beach, Hillsborough County, FL PROJECT...

330

Artificial neural network modeling of the spontaneous combustion occurring in the industrial-scale coal stockpiles with 10-18 mm coal grain sizes  

SciTech Connect (OSTI)

Companies consuming large amounts of coal should work with coal stocks in order to not face problems due to production delays. The industrial-scale stockpiles formed for the aforementioned reasons cause environmental problems and economic losses for the companies. This study was performed in a coal stock area of a large company in Konya, which uses large amounts of coal in its manufacturing units. The coal stockpile with 5 m width, 10 m length, 3 m height, and having 120 tons of weight was formed in the coal stock area of the company. The inner temperature data of the stockpile was recorded by 17 temperature sensors placed inside the stockpile at certain points. In order to achieve this goal, the electrical signal conversion of temperatures sensed by 17 temperature sensors placed in certain points inside the coal stockpile, the transfer of these electrical signals into computer media by using analog-digital conversion unit after applying necessary filtration and upgrading processes, and the record of these information into a database in particular time intervals are provided. Additionally, the data relating to the air temperature, air humidity, atmospheric pressure, wind velocity, and wind direction that are the parameters affecting the coal stockpile were also recorded. Afterwards, these measurement values were used for training and testing of an artificial neural network model. Comparison of the experimental and artificial neural network results, accuracy rates of training and testing were found to be 99.5% and 99.17%, respectively. It is shown that possible coal stockpile behavior with this artificial neural network model is powerfully estimated.

Ozdeniz, A.H.; Yilmaz, N. [Selcuk University, Konya (Turkey). Dept. of Mining Engineering

2009-07-01T23:59:59.000Z

331

Proc. 9th IEEE Int. Conference on Tools with Artificial Intelligence (ICTAI'97), Nov. 1997, Newport Beach, California, USA A Fuzzy Neural Network Approach to Classification Based on Proximity  

E-Print Network [OSTI]

. The resulting scheme is a constructive algorithm that defines fuzzy clusters of patterns. Based on observations approaches to pattern recognition that result from the combination of techniques belonging to different fields. In this sense, sev- eral models combining fuzzy systems and neural networks have been developed

Blekas, Konstantinos

332

Neural daylight control system  

E-Print Network [OSTI]

The paper describes the design, the implementation of a neural controller used in an automatic daylight control system. The automatic lighting control system (ALCS) attempt to maintain constant the illuminance at the desired level on working plane even if the daylight contribution is variable. Therefore, the daylight will represent the perturbation signal for the ALCS. The mathematical model of process is unknown. The applied structure of control need the inverse model of process. For this purpose it was used other artificial neural network (ANN) which identify the inverse model of process in an on-line manner. In fact, this ANN identify the inverse model of process + the perturbation signal. In this way the learning signal for neural controller has a better accuracy for the present application.

Grif, Horatiu Stefan

2010-01-01T23:59:59.000Z

333

Comparison of a Recurrent Neural Network PV System Model with a Traditional Component-Based PV System Model  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to User Group and UserofProteinNewsat NERSC#N/Aa Recurrent Neural

334

associative memory models: Topics by E-print Network  

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

David 1971-01-01 2 COMPLEX ASSOCIATIVE MEMORY NEURAL NETWORK MODEL FOR INVARIANT PATTERN RECOGNITION Engineering Websites Summary: COMPLEX ASSOCIATIVE MEMORY NEURAL...

335

activity relationship qsar: Topics by E-print Network  

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

Structure-Activity Relationship (QSAR) using Artificial Neural in solving non-linear pattern classification problems, we propose several different models of neural networks...

336

associative memory model: Topics by E-print Network  

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

David 1971-01-01 2 COMPLEX ASSOCIATIVE MEMORY NEURAL NETWORK MODEL FOR INVARIANT PATTERN RECOGNITION Engineering Websites Summary: COMPLEX ASSOCIATIVE MEMORY NEURAL...

337

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 7, NO. 5, SEPTEMBER 1996 1 Use of Bias Term in  

E-Print Network [OSTI]

of linear combinations of the predictors. Projection pursuit learning (PPL) proposed by Hwang et al and PPL is that the smoothers in PPR are nonparametric, whereas those in PPL are based on Hermite, that for PPL has not been thoroughly studied. In this paper, we demonstrate that PPL networks in the original

Kwok, James Tin-Yau

338

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 23, NO. 8, AUGUST 2012 1279 Spatial Gaussian Process Regression  

E-Print Network [OSTI]

unmanned underwater vehicles in [3]. A mobile sensor network is able to make sensory obser- vations projects have targeted to this research area, such as monitoring forest fires using unmanned aerial vehicles (UAVs) in [1], monitoring air quality using UAVs in [2], monitoring ocean ecology conditions using

Hu, Huosheng

339

Machine Learning Research Group Working Paper 91-2 Refining Algorithms with Knowledge-Based Neural Networks  

E-Print Network [OSTI]

Networks: Improving the Chou-Fasman Algorithm for Protein Folding* Richard Maclin Jude W. Shavlik Computer-Fasman algorithm, a method for predicting how globular proteins fold. The Chou-Fasman algorithm cannot be elegantly and discuss the need for better definitions of solution quality for the protein-folding problem. 1

Shavlik, Jude W.

340

Automated detection of cloud and cloud-shadow in single-date Landsat imagery using neural networks and spatial post-processing  

SciTech Connect (OSTI)

Use of Landsat data to answer ecological questions is contingent on the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, \\textsc{sparcs}: Spacial Procedures for Automated Removal of Cloud and Shadow. The method uses neural networks to determine cloud, cloud-shadow, water, snow/ice, and clear-sky membership of each pixel in a Landsat scene, and then applies a set of procedures to enforce spatial rules. In a comparison to FMask, a high-quality cloud and cloud-shadow classification algorithm currently available, \\textsc{sparcs} performs favorably, with similar omission errors for clouds (0.8% and 0.9%, respectively), substantially lower omission error for cloud-shadow (8.3% and 1.1%), and fewer errors of commission (7.8% and 5.0%). Additionally, textsc{sparcs} provides a measure of uncertainty in its classification that can be exploited by other processes that use the cloud and cloud-shadow detection. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of algorithms detecting vegetation change.

Hughes, Michael J. [University of Tennessee, Knoxville (UTK)] [University of Tennessee, Knoxville (UTK); Hayes, Daniel J [ORNL] [ORNL

2014-01-01T23:59:59.000Z

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


341

A wirelessly powered and controlled device for optical neural control of freely-behaving animals  

E-Print Network [OSTI]

Optogenetics, the ability to use light to activate and silence specific neuron types within neural networks in vivo and in vitro, is revolutionizing neuroscientists' capacity to understand how defined neural circuit elements ...

Wentz, Christian T.

342

Genetic attack on neural cryptography  

SciTech Connect (OSTI)

Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido [Institut fuer Theoretische Physik, Universitaet Wuerzburg, Am Hubland, 97074 Wuerzburg (Germany); Minerva Center and Department of Physics, Bar Ilan University, Ramat Gan 52900 (Israel)

2006-03-15T23:59:59.000Z

343

E-Print Network 3.0 - active network analysis Sample Search Results  

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

activity of coupled networks. The neurons... and activity propagation in coupled neural networks from rat cortical cells grown on a micro-electrode array... for parallel activity...

344

B-jet and c-jet identification with Neural Networks as well as combination of multivariate analyses for the search for of multivariate analyses for the search for single top-quark production  

SciTech Connect (OSTI)

In the first part of this diploma thesis, the current version of the KIT Flavor Separator, a neural network which is able to distinguish between tagged b-quark jets and tagged c/light-quark jets, is presented. In comparison with previous versions four new input variables are utilized and new Monte Carlo samples with a larger number of simulated events are used for the training of the neural network. It is illustrated that the output of the neural network is continuously distributed between 1 and -1, whereas b-quark jets accumulate at 1, however, c-quark jets and light-quark jets have outputs next to -1. To ensure that the network output describes observed events correctly, the shapes of all input variables are compared in simulation and data. Thus the mismodelling of any input variable is excluded. Moreover, the b jet and light jet output distributions are compared with the output of samples of observed events, which are enhanced in the particular flavor. In contrast to previous versions, no b-jet output correction function has to be calculated, because the agreement between simulation and collision data is excellent for b-quark jets. For the light-jet output, correction functions are developed. Different applications of the KIT Flavor Separator are mentioned. For example it provides a precious input to all three CDF single top quark analyses. Furthermore, it is shown that the KIT Flavor Separator is a universal tool, which can be used in every high-p{sub T} analysis that requires the identification of b-quark jets with high efficiency. As it is pointed out, a further application is the estimation of the flavor composition of a given sample of observed events. In addition a neural network, which is able to separate c-quark jets from light-quark jets, is trained. It is shown, that all three flavors can be separated in the c-net-Flavor Separator plane. As a result, the uncertainties on the estimation of the flavor composition in events with one tagged jet are cut into half. In the second part of this diploma thesis, a method for the combination of three multivariate single-top analyses using an integrated luminosity of 2.2 fb{sup -1} is presented. For this purpose the discriminants of the Likelihood Function analysis, the Matrix Element method and the Neural Network analysis are used as input variables to a neural network. Overall four different networks are trained, one for events with two or three jets and one or two SecVtx tags, respectively. Using a binned likelihood function, the outputs of these networks are fitted to the output distribution of observed events. A single top-quark production cross section of {sigma}{sub single-top} = 2.2{sub -0.7}{sup +0.8} pb is measured. Ensemble tests are performed for the calculation of the sensitivity and observed significance, which are found to be 4.8{sigma} and 3.9{sigma}, respectively. Hence the improvement of this combination is roughly 8% in comparison with sensitivities found by the individual analyses. Due to the proportionality of {sigma}{sub single-top} and |V{sub tb}|{sup 2} and under the assumption V{sub tb} >> V{sub ts}, V{sub td}, a value for |V{sub tb}| is quoted: |V{sub tb}| = 0.88{sub -0.12}{sup +0.14}(exp.) {+-} 0.07(theo.). It can be seen, that the given uncertainties are too large for a verification or falsification of the unitarity assumption of the CKM-matrix. Parallel to this combination a further combination method (NEAT-combination) has been developed. This combination uses a neural network trained with a neuroevolution technique, which optimizes the neural network architecture and weights through the use of genetic algorithms. In this analysis an improvement of roughly 12% could be reached. In figure 7.1 the current situation for the measurement of the single top-quark production cross section is summarized. After collecting more data, CDF will be able to observe single top-quark production with a significance larger than 5.0{sigma}. Nevertheless, the cross section measurement will still have large uncertainties on the level of 20%. Precise measure

Renz, Manuel; /Karlsruhe U., EKP

2008-06-01T23:59:59.000Z

345

E-Print Network 3.0 - ann modelling studies Sample Search Results  

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

predictive models. Neural networks are constructed... the search to the set of feasible networks. The network design problem ... Source: Smith, Alice E. - Department of...

346

Study of b{anti b} production in e{sup +}e{sup {minus}} annihilation at {radical}s = 29 GeV with the aid of neural networks  

SciTech Connect (OSTI)

The author presents a measurement of {sigma}(b{anti b})/{sigma}(q{anti q}) in the annihilation process e{sup +}e{sup {minus}} {yields} q{anti q} {yields} hadrons at {radical}s = 29 GeV. The analysis is based on 66 pb{sup {minus}1} of data collected between 1984 and 1986 with the TPC/2{gamma} detector at PEP. To identify bottom events, he uses a neural network with inputs that are computed from the 3-momenta of all of the observed charged hadrons in each event. He also presents a study of bias in techniques for measuring inclusive {pi}{sup {+-}}, K{sup {+-}}, and p/{anti p} production in the annihilation process e{sup +}e{sup {minus}} {yields} b{anti b} {yields} hadrons at {radical}s = 29 GeV, using a neural network to identify bottom-quark jets. In this study, charged particles are identified by a simultaneous measurement of momentum and ionization energy loss (dE/dx).

Lambert, D.J. [Univ. of California, Berkeley, CA (United States). Dept. of Physics; [Lawrence Berkeley Lab., CA (United States). Physics Div.

1994-11-01T23:59:59.000Z

347

Understanding Perception Through Neural 'Codes'  

E-Print Network [OSTI]

Perception Through Neural ĎCodesí. In: Special Issue on ďPerception Through Neural ĎCodesí. In: Special Issue on ďPerception Through Neural ĎCodesí. In: Special Issue on ď

Freeman, Walter J III

2011-01-01T23:59:59.000Z

348

Neural Networks:Neural Networks: Modeling ApplicationsModeling Applications  

E-Print Network [OSTI]

and/or integral equations representing mathematical model of a given physical process. The coefficients of these equations must be exactly known as they are used to program/adjust the coefficient inherent in analog differentiation the [differential] equation is rearranged so that it can be solved

Petriu, Emil M.

349

Using Recurrent Networks for Dimensionality Reduction  

E-Print Network [OSTI]

This report explores how recurrent neural networks can be exploited for learning high-dimensional mappings. Since recurrent networks are as powerful as Turing machines, an interesting question is how recurrent networks ...

Jones, Michael J.

1992-09-01T23:59:59.000Z

350

ARTICLE IN PRESS Neural Networks ( )  

E-Print Network [OSTI]

Voltage sensitive dye Mean field model Extracellular stimulation Inhibition a b s t r a c t The plexus population activity that has been observed in vitro using voltage sensitive dyes. The model demonstrates to layer 2/3 cortical cells (Binzegger, Douglas, & Martin, 2004). Such projections exhibit striking

Wennekers, Thomas

351

The Role of Short-Term Synaptic Plasticity in Neural Network Spiking Dynamics and in the Learning of Multiple Distal Rewards  

E-Print Network [OSTI]

firing rate for the networks tested in the synaptic weight parameter sweep. 19 Signal propagation circuitfiring rate for the networks tested in the synaptic weight parameter sweep. Signal Propagation Circuit

O'Brien, Michael John

2013-01-01T23:59:59.000Z

352

Neural Modeling and Control of Diesel Engine with Pollution Constraints  

E-Print Network [OSTI]

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identi?cation and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are ?exible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The present...

Ouladsine, Mustapha; Dovifaaz, Xavier; 10.1007/s10846-005-3806-y

2009-01-01T23:59:59.000Z

353

E-Print Network 3.0 - abrasion tests Sample Search Results  

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

neural networks, process monitoring, process optimization, engine intake Source: Smith, Alice E. - Department of Industrial and Systems Engineering, Auburn University...

354

Toward an analog neural substrate for production systems Patrick Simen (psimen@princeton.edu), Marieke Van Vugt (mkvan@princeton.edu)  

E-Print Network [OSTI]

Toward an analog neural substrate for production systems Patrick Simen (psimen existing work showing that critical features of sym- bolic production systems can be implemented oscillators. Keywords: Production system; neural network; diffusion model; random walk; reinforcement learning

van Vugt, Marieke

355

E-Print Network 3.0 - association logic networks Sample Search...  

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

memory. Memory... mapping. Adaptive Resonance Theory (ART). 7. Theory of fuzzy logic. Fuzzy sets and degree of association... NEURAL NETWORKS DESIGN (EE 578 and ME 578)...

356

Cytological Diagnosis Based on Fuzzy Neural K. Blekas and A. Stafylopatis \\Lambda , D. Kontoravdis y , A. Likas z ,  

E-Print Network [OSTI]

neural network classifier, an efficient pattern recognition approach, was used to classify benign that is based on hyperbox fuzzy sets. A hyperbox constitutes a region in the pattern space that canCytological Diagnosis Based on Fuzzy Neural Networks K. Blekas and A. Stafylopatis \\Lambda , D

Likas, Aristidis

357

Radial-Basis-Function-Network-Based Prediction of Performance and Emission Characteristics in a Bio Diesel Engine Run on WCO Ester  

E-Print Network [OSTI]

Radial basis function neural networks (RBFNNs), which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel ...

Kumar, Shiva

2012-01-01T23:59:59.000Z

358

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

E-Print Network [OSTI]

networks 641 Hydrology and Earth System Sciences, 6(4), 641­654 (2002) © EGS Multivariate synthetic associated with hydrological processes, making it valuable as a practical tool for synthetic generation backpropagation, hydrological scenario generation, multivariate time-series. Introduction It has been almost four

Paris-Sud XI, Université de

359

1997 SPECIAL ISSUE A Neural Global Workspace Model for Conscious Attention  

E-Print Network [OSTI]

1997 SPECIAL ISSUE A Neural Global Workspace Model for Conscious Attention James Newman,1 Bernard J and 3 Department of Computer Science, Yonsei University (Received 9 July 1996; accepted 24 April 1997 neural network principles. 1997 Elsevier Science Ltd. Keywords--Attention, Binding, Consciousness, Global

Memphis, University of

360

A Neural Approach for Fast Simulation of Flight Mechanics Girgio Valmrbida  

E-Print Network [OSTI]

of the aerodynamic forces acting on aircraft. Artificial Neural networks appear to be an appropriate numericalC , mC : drag, lift and pitch aerodynamic coefficients 3. Estimation of flight forces: a proposal

Paris-Sud XI, Universitť de

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to obtain the most current and comprehensive results.


361

Emergence of Attention within a Neural Population Nicolas P. Rougier and Julien Vitay  

E-Print Network [OSTI]

development (Katz & Callaway, 1992). Based on these studies, (Sirosh & Miikulainen, 1993, 1997; Miikulainen, Bednar, Choe, & Sirosh, 1997) have designed a self-organizing neural network model for the simultaneous

Paris-Sud XI, Université de

362

artificial immune pattern: Topics by E-print Network  

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

the field of Artificial Intelligence and Machine Learning. By developing abstractDynamic Pattern Recognition in Sport by Means of Artificial Neural Networks Jrgen Perl, Peter...

363

E-Print Network 3.0 - anne hansbergiga majandusministeeriumi...  

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

Techniques in Instrumentation, Measurement and Related Summary: , Denker, and Solla observed in 3, often the best ar- tificial neural network (ANN) to solve a real... -world...

364

aircraft communication systems: Topics by E-print Network  

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

and the high-speed of aircrafts Jain, Raj 5 Aircraft System Identification Using Artificial Neural Networks Engineering Websites Summary: Aircraft System Identification Using...

365

aircraft system concept: Topics by E-print Network  

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

aircraft conducting High Volume Valasek, John 4 Aircraft System Identification Using Artificial Neural Networks Engineering Websites Summary: Aircraft System Identification Using...

366

aid maximum output: Topics by E-print Network  

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

at a given geographic location. Taking the solar irradiation levels, the ambient temperature, and the Sun's position angles as inputs, a multilayer feed-forward neural network...

367

artificial extracellular matrix: Topics by E-print Network  

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

Demetri 346 Algorithms and Hardware for Implementing Artificial Neural Networks Nathan Hower Computer Technologies and Information Sciences Websites Summary: Algorithms and...

368

The neural decoding toolbox  

E-Print Network [OSTI]

Population decoding is a powerful way to analyze neural data, however, currently only a small percentage of systems neuroscience researchers use this method. In order to increase the use of population decoding, we have ...

Meyers, Ethan M.

369

Neural Control of Rhythmic Arm Movements Matthew M. Williamson  

E-Print Network [OSTI]

, 1998 Abstract In this paper we present an approach to robot arm control based on exploiting on two real robot arms, and has been used to tune into the resonant frequency of pendulums, perform multi network, Robot Manipulator, Rhythmic move- ment. 1 #12;Neural Control of Rhythmic Arm Movements 2 1

370

Network  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary)morphinanInformation Desert Southwest Regionat Cornell Batteries &NSTCurrent Issues &Network ¬Ľ Network

371

Supramolecular architectures for neural prostheses  

E-Print Network [OSTI]

Neural prosthetic devices offer a means of restoring function that have been lost due to neural damage. The first part of this thesis investigates the design of a 15-channel, low-power, fully implantable stimulator chip. ...

Theogarajan, Luke Satish Kumar

2007-01-01T23:59:59.000Z

372

E-Print Network 3.0 - analog signal pre-processing Sample Search...  

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

pre-processing Page: << < 1 2 3 4 5 > >> 1 Contributed article Fractional Fourier transform pre-processing for neural networks Summary: This study investigates fractional...

373

E-Print Network 3.0 - anti-psychotic maintenance medication Sample...  

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

Software... Safety and Security Neural networks IVARA www.ivara.com Reliability centered maintenance solutions... maintenance solutions RCM toolkit (report generation)...

374

E-Print Network 3.0 - adaptive trajectory tracking Sample Search...  

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

Sciences 48 On the Classification of Moving Objects in Image Sequences Using 3D Adaptive Recursive Tracking Filters and Neural Networks Summary: On the Classification of...

375

E-Print Network 3.0 - anier aleksander arkovski Sample Search...  

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

92 Node Allocation and Topographical Encoding NATEnet for Inverse Kinematics of a 6DOF Robot Arm Summary: . Aleksander and J. Taylor, editors, Arti cal Neural Networks, pages...

376

E-Print Network 3.0 - adaptive power control Sample Search Results  

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controller, which does not need any... Coordinating Control of a Power System with Wind Farm Integration and Multiple FACTS Devices", Neural Networks... , "Coordinated Reactive...

377

E-Print Network 3.0 - arrow falcon exporters Sample Search Results  

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

NETWORKS, VOL. 19, NO. 2, FEBRUARY 2008 Integrating Temporal Difference Methods and Summary: , the proposed neural model, called TD fusion architecture for learning, cognition,...

378

E-Print Network 3.0 - affects patient survival Sample Search...  

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

12 Application of Artificial Neural Network-Based Survival Analysis on Two Breast Cancer Datasets Summary: -free survival time (DFS) because these patients may change doctors,...

379

E-Print Network 3.0 - avoiding catastrophic infall Sample Search...  

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

S. (1997) Avoiding catastrophic forgetting by coupling two reverberating neural... French, R. M. (2003) Catastrophic Forgetting in Connectionist Networks. In ... Source:...

380

E-Print Network 3.0 - anne ingver ilmar Sample Search Results  

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

Techniques in Instrumentation, Measurement and Related Summary: , Denker, and Solla observed in 3, often the best ar- tificial neural network (ANN) to solve a real... -world...

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While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
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to obtain the most current and comprehensive results.


381

E-Print Network 3.0 - anne kuusksalu klli Sample Search Results  

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

Techniques in Instrumentation, Measurement and Related Summary: , Denker, and Solla observed in 3, often the best ar- tificial neural network (ANN) to solve a real... -world...

382

E-Print Network 3.0 - ann kivistik klliki Sample Search Results  

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

Techniques in Instrumentation, Measurement and Related Summary: , Denker, and Solla observed in 3, often the best ar- tificial neural network (ANN) to solve a real... -world...

383

E-Print Network 3.0 - argonne-notre dame bgo Sample Search Results  

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

Sciences 34 M. A. Sartori and P. J. Antsaklis, "Neural Network Implementations for Control Scheduling," Technical Report 91-04-02, Dept. of Electrical Engineering,...

384

E-Print Network 3.0 - anti-c antibodies diagnosed Sample Search...  

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

trouble diagnosing, and it has been a research hot spot after the neural network, fuzzy logic... immunodominance, the epitope is called a dominant epitope, immunodominance is...

385

E-Print Network 3.0 - area technical baseline Sample Search Results  

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

presented strategy of automatic baseline detection in chromatograms... combines fuzzy logic and neural network approaches. It is based on a verbal description of a baseline...

386

E-Print Network 3.0 - apnea syndrome osas Sample Search Results  

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

2009 1057 Automated Scoring of Obstructive Sleep Apnea Summary: signals. Index Terms--ECG, neural networks (NNs), obstructive sleep apnea (OSA), sleep study, wavelet. I... ....

387

Communication Load Reduction for Neural Network Implementations  

E-Print Network [OSTI]

the total amount of communication load, followed by a placement of partitions onto proces- sors 3]. We

Behnke, Sven

388

Neural networks predict well inflow performance  

E-Print Network [OSTI]

Predicting well inflow performance relationship accurately is very important for production engineers. From these predictions, future plans for handling and improving well performance can be established. One method of predicting well inflow...

Alrumah, Muhammad K.

2004-09-30T23:59:59.000Z

389

Master Thesis Combined Neural Networks and  

E-Print Network [OSTI]

in steel design Joo, Min Sung ( ) Department of Ferrous Technology (Computational Metallurgy) Graduate of Ferrous Technology (Computational Metallurgy) Graduate Institute of Ferrous Technology Pohang University of Ferrous Technology (Computational Metallurgy) Pohang, Korea June 23th , 2008 Approved by #12;Combined

Cambridge, University of

390

Conceptualization and image understanding by neural networks  

E-Print Network [OSTI]

Conceptualization and Fuzzy Semantics 2. Conceptual Associations 3. Pattern-Concept Association 4. Concept-Pattern Association 5. Concept-Concept Association 6. Conceptual Analysis 7. Conceptualization Depth vs Resolution Depth . 8. Focusing of Attention 9.... Comparison Between Gravity Algorithm and Spread- ing Activation 3. Segmentation . 4. Pattern Classification Stage . 5. Invariances by the Fourier-Mellin Transform B. Knowledge Base . 16 19 19 21 22 23 23 25 26 27 29 30 32 32 32 35 37 48...

Gudipalley, Chandu

1993-01-01T23:59:59.000Z

391

Neural networks Chapter 19, Sections 15  

E-Print Network [OSTI]

= a parameterized family of nonlinear functions: a5 = g(W3,5 · a3 + W4,5 · a4) = g(W3,5 · g(W1,3 · a1 + W2,3 · a2) + W4,5 · g(W1,4 · a1 + W2,4 · a2)) Chapter 19, Sections 1­5 8 #12;Perceptrons Input Units Units Output

Hawick, Ken

392

Last update: December 4, 2008 Neural networks  

E-Print Network [OSTI]

family of nonlinear functions: a5 = g(W3,5 · a3 + W4,5 · a4) = g(W3,5 · g(W1,3 · a1 + W2,3 · a2) + W4,5 · g(W1,4 · a1 + W2,4 · a2)) Adjusting weights changes the function: do learning this way! CMSC 421

Nau, Dana S.

393

Neural networks Chapter 20, Section 5  

E-Print Network [OSTI]

= a parameterized family of nonlinear functions: a5 = g(W3,5 · a3 + W4,5 · a4) = g(W3,5 · g(W1,3 · a1 + W2,3 · a2) + W4,5 · g(W1,4 · a1 + W2,4 · a2)) Adjusting weights changes the function: do learning this way

Nie, Jian-Yun

394

Secret sharing using artificial neural network  

E-Print Network [OSTI]

.................................................. 5 1.4 Dissertation Outline.................................................................. 7 II SECRET SHARING ? STATE OF THE ART.................................... 9 2.1 General Model for Secret Sharing Schemes....3.4 Secret sharing homomorphism..................................... 20 2.3.5 Linear secret sharing schemes...................................... 21 2.4 Secret Sharing Schemes with Extended Capabilities............... 22 2.4.1 Protecting against...

Alkharobi, Talal M.

2004-11-15T23:59:59.000Z

395

Nonlinear principal component analysis by neural networks  

E-Print Network [OSTI]

bottleneck, the NLPCA is able to extract periodic or wave modes. The Lorenz (1963) 3-component chaotic system nonlinear empirical modelling methods originating from the field of artificial intelligence, raises the hope that the linear restriction in our analysis of environmental datasets may finally be lifted (Hsieh and Tang, 1998

Hsieh, William

396

Nonlinear principal component analysis by neural networks  

E-Print Network [OSTI]

bottleneck, the NLPCA is able to extract periodic or wave modes. The Lorenz (1963) 3­component chaotic system, a class of powerful nonlinear empirical modelling methods originating from the field of artificial be lifted (Hsieh and Tang, 1998). Various NN methods have been developed for performing PCA (Oja, 1982

Hsieh, William

397

Discrete Tomography: A Neural Network Approach  

E-Print Network [OSTI]

Abstract Tomography tries to reconstruct an object from a number of projections in multiple directions quality reconstructions from a limited set of projections, while avoiding image artifacts that are often present in traditional approaches. 1 Introduction Tomography, or more especially computed tomography

Kosters, Walter

398

Automated intelligent training of backpropagation neural networks  

E-Print Network [OSTI]

(. '0?lf&utdt]0??1 Lh(' bl"l?l Is a kind of ref txatio??ysteu?? ivl&i& h co?&f?&till iot& proc((d? to?ati?fy a?u?&her of const&iu?ls, t hc ('on&i&'('&10?? I)(&IIV('(:1&?c?1'0?? cd? I)(?c('? as I'( pl('s& utulg (0??tin?&la 0&1 th( (0-ocr?i'cn('cs 01 p...?1's of 0('ut'0??. 1 fl(' ?v?l('?1?hu?ld I&( s(( u ???ettl?&g?ltu a solut&on ratf&cr Lf&an calculiit&ug ?solut&o?. C'on/i]ious &I&oil&(/?/i/i/ o/' I]I/'o] ?&olio?. An in&portatit f(at?r& of ?cural information f&1'occssiug is thc & o?tin&?1? avd&1...

Rajan, V

1991-01-01T23:59:59.000Z

399

Calibrating Artificial Neural Networks by Global Optimization  

E-Print Network [OSTI]

(2009) A global optimization study on the devolatilisation kinetics of coal, biomass and waste fuels. .... Proc. IEEE ISCAS, Bangkok, Thailand, May 2003, V725-728.

Janos Pinter

2010-07-21T23:59:59.000Z

400

Proc. 9th IEEE Int. Conference on Tools with Artificial Intelligence (ICTAI'97), Nov. 1997, Newport Beach, California, USA A Fuzzy Neural Network Approach to Classification Based on Proximity  

E-Print Network [OSTI]

. This property has been widely exploited in pattern recognition approaches. The proposed fuzzy neural approach on geometrical fuzzy sets. Starting from the construction of the Voronoi diagram of the training patterns exclusively to one of the pattern classes. The resulting scheme is a constructive algorithm that defines fuzzy

Likas, Aristidis

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

Cyborg Bugs... and Neural Dust  

E-Print Network [OSTI]

Cyborg Bugs... and Neural Dust Michel M. Maharbiz © 2014 D.J. Seo Elad Alon system Seo D, et al. "Neural Dust: An Ultrasonic, Low Power SoluNon for Chronic Brain-Machine Interfaces," arXiv, Jul. 2013 Seo D, et al. "In Vitro Characteriza

California at Irvine, University of

402

NEURAL CIRCUITS ORIGINAL RESEARCH ARTICLE  

E-Print Network [OSTI]

NEURAL CIRCUITS ORIGINAL RESEARCH ARTICLE published: 14 May 2010 doi: 10.3389/fncir.2010.00013 Frontiers in Neural Circuits www.frontiersin.org May 2010 | Volume 4 | Article 13 | 1 Signal processing a more holistic standpoint (Roberts, 1979; Bialek et al., 1991). For a continuously firing cell

Trauner, Dirk

403

Associative memory in phasing neuron networks  

SciTech Connect (OSTI)

We studied pattern formation in a network of coupled Hindmarsh-Rose model neurons and introduced a new model for associative memory retrieval using networks of Kuramoto oscillators. Hindmarsh-Rose Neural Networks can exhibit a rich set of collective dynamics that can be controlled by their connectivity. Specifically, we showed an instance of Hebb's rule where spiking was correlated with network topology. Based on this, we presented a simple model of associative memory in coupled phase oscillators.

Nair, Niketh S [ORNL; Bochove, Erik J. [United States Air Force Research Laboratory, Kirtland Air Force Base; Braiman, Yehuda [ORNL

2014-01-01T23:59:59.000Z

404

International Journal of Neural Systems, Vol. 0, No. 0 (April, 2000) 0000 c World Scientific Publishing Company  

E-Print Network [OSTI]

International Journal of Neural Systems, Vol. 0, No. 0 (April, 2000) 00­00 c World Scientific Publishing Company DISTRIBUTED SENSOR NETWORKS: A CELLULAR NONLINEAR NETWORK PERSPECTIVE MARTIN HAENGGI Dept increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions

Haenggi, Martin

405

Wavelets, Self-organizing Maps and Artificial Neural Nets for Predicting Energy Use and Estimating Uncertainties in Energy Savings in Commercial Buildings  

E-Print Network [OSTI]

This dissertation develops a "neighborhood" based neural network model utilizing wavelet analysis and Self-organizing Map (SOM) to predict building baseline energy use. Wavelet analysis was used for feature extraction of the daily weather profiles...

Lei, Yafeng

2010-01-14T23:59:59.000Z

406

Demultiplexer circuit for neural stimulation  

DOE Patents [OSTI]

A demultiplexer circuit is disclosed which can be used with a conventional neural stimulator to extend the number of electrodes which can be activated. The demultiplexer circuit, which is formed on a semiconductor substrate containing a power supply that provides all the dc electrical power for operation of the circuit, includes digital latches that receive and store addressing information from the neural stimulator one bit at a time. This addressing information is used to program one or more 1:2.sup.N demultiplexers in the demultiplexer circuit which then route neural stimulation signals from the neural stimulator to an electrode array which is connected to the outputs of the 1:2.sup.N demultiplexer. The demultiplexer circuit allows the number of individual electrodes in the electrode array to be increased by a factor of 2.sup.N with N generally being in a range of 2-4.

Wessendorf, Kurt O; Okandan, Murat; Pearson, Sean

2012-10-09T23:59:59.000Z

407

A Recurrent Neural Multi-Model for Mechanical Systems Dynamics Compensation  

E-Print Network [OSTI]

Mexico D.F., Mexico ** Institute of Information Technologies, 1113 Sofia Abstract: The paper proposed nonlinear mechanical plants with backlash. The parameters and states of the local recurrent neural network of the plant model. For example, N a r e n d r a and P a r t h a s a r a t h y [5], applied FFNN for system

Borissova, Daniela

408

Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains  

E-Print Network [OSTI]

We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal constraints from experimental spike trains. This is an extension of two papers [10] and [4] who proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows to properly handle memory effects in spike statistics, for large sized neural networks.

Nasser, Hassan

2014-01-01T23:59:59.000Z

409

Acute Myocardial Infarction: Analysis of the ECG Using Arti cial Neural  

E-Print Network [OSTI]

Acute Myocardial Infarction: Analysis of the ECG Using Arti#12;cial Neural Networks Mattias Ohlsson, using the 12-lead ECG. Features from the ECGs were extracted using principal component analysis, which allows for a small number of e#11;ective indicators. A total of 4724 pairs of ECGs, recorded

Lunds Universitet,

410

azidothymidine azt treatment: Topics by E-print Network  

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

were investigated using murine and (more) Lee, Sung-Hack 2008-01-01 6 Classification of brain compartments and head injury lesions by neural networks applied to magnetic...

411

applied transient dynamic: Topics by E-print Network  

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

21 22 23 24 25 Next Page Last Page Topic Index 1 Neural networks with transient state dynamics Astrophysics (arXiv) Summary: We investigate dynamical systems characterized by a...

412

Signal Processing for Neural Spike Trains  

E-Print Network [OSTI]

Editorial: Signal processing and statistics have been playing a pivotal role in computational neuroscience and neural engineering research.

Berger, Theodore W.

413

Neural Plasticity of Development and Learning  

E-Print Network [OSTI]

Neural Plasticity of Development and Learning Galvan, 2010 Presented by Kristen Morin and Sunil Patel I. Defining Development and Learning II. Neural Plasticity III. Progressive and Regressive Changes with Learning IV. Plasticity of Developmental Timing V. Neural Mechanism- Same or Different? VI. Methodological

Gabrieli, John

414

Network Management Network Management  

E-Print Network [OSTI]

that pertain to the operation, administration, maintenance, and provisioning of networked systems ∑ Operation deals with keeping the network up (and the service provided by the network) ∑ Administration involvesNetwork Management Pag. 1 Network Management Andrea Bianco Telecommunication Network Group Network

415

Google matrix analysis of directed networks  

E-Print Network [OSTI]

In past ten years, modern societies developed enormous communication and social networks. Their classification and information retrieval processing become a formidable task for the society. Due to the rapid growth of World Wide Web, social and communication networks, new mathematical methods have been invented to characterize the properties of these networks on a more detailed and precise level. Various search engines are essentially using such methods. It is highly important to develop new tools to classify and rank enormous amount of network information in a way adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency on various examples including World Wide Web, Wikipedia, software architecture, world trade, social and citation networks, brain neural networks, DNA sequences and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos and Random Matrix theory.

Leonardo Ermann; Klaus M. Frahm; Dima L. Shepelyansky

2014-09-01T23:59:59.000Z

416

Smart Engineering System Design, vol. 4:243-252, 2002 NETWORK CONGESTION ARBITRATION AND SOURCE PROBLEM  

E-Print Network [OSTI]

to address the problem. However, these tools simply give the network administrator a great deal1 Smart Engineering System Design, vol. 4:243-252, 2002 NETWORK CONGESTION ARBITRATION AND SOURCE PROBLEM PREDICTION USING NEURAL NETWORKS J. ALAN BIVENS Computer Science Rensselaer Polytechnic Institute

Varela, Carlos

417

Hitzler Neural-Symbolic Integration Osnabrck Germany November 2007 Neural-Symbolic Integration  

E-Print Network [OSTI]

Hitzler Neural-Symbolic Integration Osnabr√ľck Germany November 2007 1/49 AIFBAIFB Neural-Symbolic Integration PD Dr. Pascal Hitzler AIFB, Universit√§t Karlsruhe Osnabr√ľck, Germany, November 2007 #12;Hitzler Neural-Symbolic Integration Osnabr√ľck Germany November 2007 2/49 AIFBAIFB PD Dr. Pascal Hitzler

Hitzler, Pascal

418

Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control  

E-Print Network [OSTI]

It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset.

Rodrigo Laje; Dean V. Buonomano

2012-10-07T23:59:59.000Z

419

FACILITATORY NEURAL DYNAMICS FOR PREDICTIVE EXTRAPOLATION  

E-Print Network [OSTI]

FACILITATORY NEURAL DYNAMICS FOR PREDICTIVE EXTRAPOLATION A Dissertation by HEE JIN LIM Submitted DYNAMICS FOR PREDICTIVE EXTRAPOLATION A Dissertation by HEE JIN LIM Submitted to Texas A&M University: Computer Science #12;iii ABSTRACT Facilitatory Neural Dynamics for Predictive Extrapolation. (August 2006

Choe, Yoonsuck

420

In Vivo Analysis of Engrafted Adult Hippocampal Neural Progenitors  

E-Print Network [OSTI]

. Robertson, Joseph Peltier, and David V. Schaffer Abstract A neural degenerative disease is characterized by the deterioration of neural tissue and subsequent loss of function. The in vivo engraftment of neural stem cells function. In addition, by studying the behavior of engrafted neural stem cells in healthy and diseased

Schaffer, David V.

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

Network Chimera Network Chimera  

E-Print Network [OSTI]

Network Chimera Network Chimera Objective Chimera aims to understand how the network properties enough with limited resources. The Chimera team is cross-disciplinary, and includes computer scientists Impact The original hypothesis of Chimera was that a physical network could be reduced to a graph

422

Independent optical excitation of distinct neural populations  

E-Print Network [OSTI]

Optogenetic tools enable examination of how specific cell types contribute to brain circuit functions. A long-standing question is whether it is possible to independently activate two distinct neural populations in mammalian ...

Klapoetke, Nathan Cao

423

N400: Neural Generators Functional Significance  

E-Print Network [OSTI]

direct manifestation of electrochemical communication between neurons ­ fMRI indirect manifestation of neural activity via oxygen consumption · Signal:Noise Considerations ­ In both methods, some brain

Coulson, Seana

424

A miniature, implantable wireless neural stimulation system  

E-Print Network [OSTI]

In this thesis, I present the design of a wireless neural stimulation system. The system consists of an external transmitter, controllable through a computer interface, and a miniature, implantable wireless receiver and ...

Arfin, Scott K. (Scott Kenneth)

2006-01-01T23:59:59.000Z

425

A framework for simulating and estimating the state and functional topology of complex dynamic geometric networks  

E-Print Network [OSTI]

We present a framework for simulating signal propagation in geometric networks (i.e. networks that can be mapped to geometric graphs in some space) and for developing algorithms that estimate (i.e. map) the state and functional topology of complex dynamic geometric net- works. Within the framework we define the key features typically present in such networks and of particular relevance to biological cellular neural networks: Dynamics, signaling, observation, and control. The framework is particularly well-suited for estimating functional connectivity in cellular neural networks from experimentally observable data, and has been implemented using graphics processing unit (GPU) high performance computing. Computationally, the framework can simulate cellular network signaling close to or faster than real time. We further propose a standard test set of networks to measure performance and compare different mapping algorithms.

Marius Buibas; Gabriel A. Silva

2010-06-22T23:59:59.000Z

426

Neural network computation with DNA strand displacement cascades  

E-Print Network [OSTI]

is Alan Turing (1 1 1 1). ? ? 0 ? 1 0 0 0 Human: The scientist I am thinking of was not born in the 20th Alan Turing 0 0 1 1 Claude Shannon 1 0 0 0 Santiago Ramon y Cajal A "read your mind" game Figure S1

Bruck, Jehoshua (Shuki)

427

adult neural progenitor: Topics by E-print Network  

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

17 18 19 20 21 22 23 24 25 Next Page Last Page Topic Index 301 The binary progenitor of Tycho Brahe's 1572 supernova Astrophysics (arXiv) Summary: The brightness of type Ia...

428

Neural Network Modeling of Abrasive Flow Machining Alice E. Smith  

E-Print Network [OSTI]

-line controller for abrasive flow machining of automotive engine intake manifolds. The process is only observable of critical components. It has been applied in the aerospace, automotive, electronic and die-making industries that the Ford Contour SVT intake manifold will be an #12;aluminum alloy. An optimal intake manifold would

Smith, Alice E.

429

Evolutionary, developmental neural networks for robust robotic control  

E-Print Network [OSTI]

The use of artificial evolution to synthesize controllers for physical robots is still in its infancy. Most applications are on very simple robots in artificial environments, and even these examples struggle to span the ...

Adams, Bryan (Bryan Paul), 1977-

2006-01-01T23:59:59.000Z

430

Structural Impairment Detection Using Arrays of Competitive Artificial Neural Networks  

E-Print Network [OSTI]

railroad functionality has perpetuated an increase in track and structure expenditures from $29.31/ mile in 1955 to $40.16/ mile in 2006 (Weatherford et al. 2006). Engineering decision making concerning the huge inventory of bridges in civil...

Story, Brett

2012-07-16T23:59:59.000Z

431

Paraphrastic language models and combination with neural network language models  

E-Print Network [OSTI]

-gram models of nat- ural language. Computational Linguistics 18(4) pp.467-470. [3] G. Cao, J-Y Nie & J. Bai (2005). Integrating word relation- ships into language models, in Proc. ACM SIGIR2005, pp. 298-305, Salvador, Brazil. [4] Z. Dong & Q. Dong (2006). How...

Liu, X.; Gales, M. J. F; Woodland, P. C.

2014-07-17T23:59:59.000Z

432

A Neural Network Model of Visual Tilt Aftereffects  

E-Print Network [OSTI]

RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map and its lateral connections are shown to result in tilt aftereffects over short time scales in the adult. The model allows observing large numbers of neurons and connections simultaneously, making it possible to relate higher-level phenomenato low-level events, which is difficult to do experimentally. The results give computational support for the idea that direct tilt aftereffects arise from adaptive lateral interactions between feature detectors, as has long been surmised. They also suggest that indirect effects could result from the conservation of synaptic resourcesduring this process. The model thus provides a unified computational explanation of self-organization and both direct and indirect tilt aftereff...

1997-01-01T23:59:59.000Z

433

Characterization of Shape Memory Alloys Using Artificial Neural Networks  

E-Print Network [OSTI]

computational learning method that intends to teach a machine how to perform a given task. The method seeks to accomplish this, however, not by specifying specifics rules. Rather, it does this by allowing the machine to try procedures either at random... to be more effective than previous reinforcement learning approaches due to the fact that it does not necessitate development of a relationship between temper- ature and applied voltage; furthermore, this voltageĖstrain approach is shown to be more accurate...

Henrickson, James V

2014-04-28T23:59:59.000Z

434

Experimental Implementation of a Hopfield Neural Network Using DNA Molecules  

E-Print Network [OSTI]

site analysis for diagnosis of sickle cell anemia. Ē Sciencesite analysis for diagnosis of sickle cell anemia. Ē Science

Karabay, Dundar

2010-01-01T23:59:59.000Z

435

Use of autoassociative neural networks for sensor diagnostics  

E-Print Network [OSTI]

faulty sensors. E-AANN uses a secondary optimization process to identify and reconstruct sensor faults. Two common types of sensor faults are investigated, drift error and shift or offset error. In the case of drift error, the sensor error occurs...

Najafi, Massieh

2005-02-17T23:59:59.000Z

436

Neural network based design of cellular manufacturing systems  

E-Print Network [OSTI]

systems do". The simple elements are called processing units [PU) and are the parallel of the neurons of the nervous system. A simple PU is shown in Figure 3. Between two PU's there is a dzrected interconnection which is the parallel of the synapses... connecting the neurons in the nervous system. The direction of the interconnection between two PU's decide which unit receives input from the other. The inputs to the PU j is the output from other PU's along the interconnections directed towards j or input...

Ramachandran, Satheesh

1990-01-01T23:59:59.000Z

437

Retrofit Analysis of HVAC Systems Using Artificial Neural Networks.  

E-Print Network [OSTI]

??This thesis aims to explore the benefits of retrofits applied to the compressor and burner in the AC and furnace respectively and how best toÖ (more)

Anand, Anish

2014-01-01T23:59:59.000Z

438

The Neural Network Pushdown Automaton: Model, Stack and Learning Simulations  

E-Print Network [OSTI]

.L. Gilesb,c , H.H. Chena,b and Y.C. Leea,b a Laboratory For Plasma Research, b Institute for Advanced

Giles, C. Lee

439

Use of Artificial Neural Networks Process Analyzers: A Case Study  

E-Print Network [OSTI]

to be used both as power source and for processsing purposes. They consist of a furnace, where air and fuel are combined and burned to produce combustion gases to a water-tube system. The tubes are connected to the steam drum, where the generated water vapor is withdrawn. Optimization of the operation of boilers can

Ghouti, Lahouari

440

GAUSSIAN PROCESSES A Replacement for Supervised Neural Networks?  

E-Print Network [OSTI]

of Physics, Cambridge University. Cavendish Laboratory, Madingley Road, Cambridge, CB3 0HE. United Kingdom and Rasmussen (1996) and Gibbs (1997). My lectures feature a sequence of computer demonstrations written

MacKay, David J.C.

Note: This page contains sample records for the topic "back-propagation neural network" from the National Library of EnergyBeta (NLEBeta).
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they are not comprehensive nor are they the most current set.
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to obtain the most current and comprehensive results.


441

absolute deviation neural: Topics by E-print Network  

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

Summary: Engineering, University of Osijek, Kneza Trpimira bb, HR-31000 Osijek, Croatia, E-mail: rcupec@etfos.hr 3 Faculty of Electrical Engineering, University of Osijek,...

442

Neural network modelling of hot deformation of austenite  

E-Print Network [OSTI]

Professor A. H. Windle for the provision of laboratory facilities in the department of Materials Science. Phil. course, either in organising the course in this first year or by lecturing. 6 #12;Abstract logarithms exp10 base 10 exponential function Q activation energy G Free energy F Helmholtz energy F force

Cambridge, University of

443

Facility Power Usage Prediction with Artificial Neural Networks.  

E-Print Network [OSTI]

??Residential and commercial buildings accounted for about 68% of the total U.S. electricity consumption in 2002. Improving the energy efficiency of buildings can save energy,Ö (more)

Wan, Sunny

2009-01-01T23:59:59.000Z

444

Adaptive Control Schemes Based on Recurrent Trainable Neural Networks  

E-Print Network [OSTI]

-Mail: baruch@ctrl.cinvestav.mx 2 Institute of Information Technologies, 1113 Sofia Abstract: The aim, according to the structure of the plant model. For example, N a r e n d r a and P a r t h a s a r a t h y non-linear plants. They considered four plant models with a given structure and supposed

Borissova, Daniela

445

asian pacific neural: Topics by E-print Network  

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

of 27 Ocean-atmosphere-land feedbacks on the western North Pacific-East Asian summer climate Environmental Sciences and Ecology Websites Summary: limiting poleward extent of...

446

Fault Tolerant Artificial Neural Networks Dhananjay. S. Phatak  

E-Print Network [OSTI]

and Applications Conference, May 1995, Utica/Rome, NY) ABSTRACT This paper investigates improved training proce to some extent. However, a brute force method of replica- tions proposed in [1, 2] seems to achieve is not enough; better learning algorithms and synthesis methods must be developed in order to achieve

Phatak, Dhananjay S.

447

Neural Network Models for Antony Lam Amar Kaheja  

E-Print Network [OSTI]

of fabric properties for the apparel textile industry and some industrial textile applications. This unique than the RBF method but the RBF method was the fastest when it came to training. Comparisons of the two models as well ss comparisons of the same models using different parameters are presented. It was also

Raheja, Amar

448

URBAN OZONE CONCENTRATION FORECASTING WITH ARTIFICIAL NEURAL NETWORK IN CORSICA  

E-Print Network [OSTI]

Perceptron; Ozone concentration. 1. Introduction Tropospheric ozone is a major air pollution problem, both, Ajaccio, France, e-mail: balu@univ-corse.fr Abstract: Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air

Boyer, Edmond

449

Optimization of neural network feedback control systems using automatic differentiation  

E-Print Network [OSTI]

Optimal control problems can be challenging to solve, whether using analytic or numerical methods. This thesis examines the application of an adjoint method for optimal feedback control, which combines various algorithmic ...

Rollins, Elizabeth, S.M. Massachusetts Institute of Technology

2009-01-01T23:59:59.000Z

450

Bayesian methods for gravitational waves and neural networks  

E-Print Network [OSTI]

. . . . . . . . . . . . . . . . . . . . . 152 6.4.2 Fast Error Calculation . . . . . . . . . . . . . . . . . . . . . 157 6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 7 Conclusions and Future Work 165 7.1 Summary of Results... slowly compared to c, then the solution to the linearised Einstein equation can be given by the compact- source approximation (a full derivation and further details on the following analysis can be found in [5]), hĮĶ?(ct,~x) =? 4G c4 r ? T Ķ?(ct? r,~y)d3~y...

Graff, Philip B.

2012-10-09T23:59:59.000Z

451

Pattern Classification with the BCM Neural Network Stanislav Poljovka and  

E-Print Network [OSTI]

ŇłnuŇłskov'a Department of Computer Science and Engineering, Slovak Technical University, IlkoviŇłcova 3, 812 19 Bratislava visual cortex [1]. Later it was used for explanation of the experience¬≠dependent plasticity in the mature Theta_M 4.0 Theta_M 3.0 Figure 1: The modification function OE for two diferent values of ` M

Benuskova, Luba

452

Applications of Artificial Neural Networks (ANNs) to Rotating Equipment  

E-Print Network [OSTI]

including (but not limited to) oil and gas industries, petrochemical plants, power plants, transmission.asgari@pg.canterbury.ac.nz Abstract Rotating equipment is the beating heart of nearly all industrial plants and specifically plays) [1]. Today, a variety of rotating equipment is being used widely in different industrial plants

Sainudiin, Raazesh

453

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

E-Print Network [OSTI]

vehicle (HEV) are more complex than those of the internal combustion engine-only vehicle because they have the appropriate power split between the electric motor and the engine to minimize fuel consumption and emissions of CI techniques. The Prius powertrain uses a planetary gear mechanism to connect an internal combustion

Prokhorov, Danil

2008-01-01T23:59:59.000Z

454

Robust exponential memory in Hopfield networks  

E-Print Network [OSTI]

The Hopfield recurrent neural network is an auto-associative distributed model of memory. This architecture is able to store collections of generic binary patterns as robust attractors; i.e., fixed-points of the network dynamics having large basins of attraction. However, the number of (randomly generated) storable memories scales at most linearly in the number of neurons, and it has been a long-standing question whether robust super-polynomial storage is possible in recurrent networks of linear threshold elements. Here, we design sparsely-connected Hopfield networks on $n$-nodes having \\[\\frac{2^{\\sqrt{2n} + \\frac{1}{4}}}{n^{1/4} \\sqrt{\\pi}}\\] graph cliques as robust memories by analytically minimizing the probability flow objective function over these patterns. Our methods also provide a biologically plausible convex learning algorithm that efficiently discovers these networks from training on very few sample memories.

Christopher Hillar; Ngoc M. Tran

2014-11-17T23:59:59.000Z

455

Proceedings of the International Workshop on Neural Networks and Neurocontrol, 1997 Neural Network and Machine Learning Laboratory  

E-Print Network [OSTI]

s t ? We assume the existence of some evaluation function, f, that will determine whether or not one plant as a neurocontroller. Combination of NN and EC technology is becoming more prevalent and usually focuses on using EC work similar in flavor to this approach does exist including [Mia96], [Mon89], and [Rom93]. Section 2

Martinez, Tony R.

456

Proceedings of the International Workshop on Neural Networks and Neurocontrol, 1997 Neural Network and Machine Learning Laboratory  

E-Print Network [OSTI]

the existence of some evaluation function, f, that will determine whether or not one plant state is better than as a neurocontroller. Combination of NN and EC technology is becoming more prevalent and usually focuses on using EC work similar in flavor to this approach does exist including [Mia96], [Mon89], and [Rom93]. Section 2

Martinez, Tony R.

457

THE DEPARTMENT OF COGNITIVE AND NEURAL SYSTEMS The Department of Cognitive and Neural Systems (CNS) provides advanced training and  

E-Print Network [OSTI]

1 THE DEPARTMENT OF COGNITIVE AND NEURAL SYSTEMS The Department of Cognitive and Neural Systems computational neuroscience, cognitive science, and neuromorphic systems. The biological training includes study; recognition learning, categorization, and long-term memory; cognitive information processing; self

Finzi, Adrien

458

Neural Maps for Mobile Robot Navigation Michail G. Lagoudakis  

E-Print Network [OSTI]

Neural Maps for Mobile Robot Navigation Michail G. Lagoudakis Department of Computer Science Duke of Southwestern Louisiana Lafayette, LA 70504 maida@cacs.usl.edu Abstract Neural maps have been recently proposed of neural maps to mobile robot navigation with fo- cus on efficient implementations. It is suggested

Lagoudakis, Michail G.

459

Exploratory Analysis of Concept and Document Spaces with Connectionist Networks  

E-Print Network [OSTI]

. Exploratory analysis is an area of increasing interest in the computational linguistics arena. Pragmatically speaking, exploratory analysis may be paraphrased as natural language processing by means of analyzing large corpora of text. Concerning the analysis, appropriate means are statistics, on the one hand, and artificial neural networks, on the other hand. As a challenging application area for exploratory analysis of text corpora we may certainly identify text databases, be it information retrieval or information filtering systems. With this paper we present recent findings of exploratory analysis based on both statistical and neural models applied to legal text corpora. Concerning the artificial neural networks, we rely on a model adhering to the unsupervised learning paradigm. This choice appears naturally when taking into account the specific properties of large text corpora where one is faced with the fact that input-output-mappings as required by supervised learning models ca...

1999-01-01T23:59:59.000Z

460

Experimental results from a network-assisted PID controller  

SciTech Connect (OSTI)

The results presented here are a continuation of studies on a neural-network-based controller. Part 1 is a summary of the previous studies, and Part 2 presents new results and offers some novel techniques used for training the network and making the entire package easier to use. The two major additions are (1) efficient use of training data for dramatically reducing memory requirements and (2) incorporation of a PID algorithm for performing control during training periods.

Curtiss, P.S. [Univ. of Colorado, Boulder, CO (United States). Joint Center for Energy Management

1996-11-01T23:59:59.000Z

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

Emergence of Attention within a Neural Population  

E-Print Network [OSTI]

We present a dynamic model of attention based on the Continuum Neural Field Theory that explains attention as being an emergent property of a neural population. This model is experimentally proved to be very robust and able to track one static or moving target in the presence of very strong noise or in the presence of a lot of distractors, even more salient than the target. This attentional property is not restricted to the visual case and can be considered as a generic attentional process of any spatio-temporal continuous input.

462

Extended Mixture of MLP Experts by Hybrid of Conjugate Gradient Method and Modified Cuckoo Search  

E-Print Network [OSTI]

This paper investigates a new method for improving the learning algorithm of Mixture of Experts (ME) model using a hybrid of Modified Cuckoo Search (MCS) and Conjugate Gradient (CG) as a second order optimization technique. The CG technique is combined with Back-Propagation (BP) algorithm to yield a much more efficient learning algorithm for ME structure. In addition, the experts and gating networks in enhanced model are replaced by CG based Multi-Layer Perceptrons (MLPs) to provide faster and more accurate learning. The CG is considerably depends on initial weights of connections of Artificial Neural Network (ANN), so, a metaheuristic algorithm, the so-called Modified Cuckoo Search is applied in order to select the optimal weights. The performance of proposed method is compared with Gradient Decent Based ME (GDME) and Conjugate Gradient Based ME (CGME) in classification and regression problems. The experimental results show that hybrid MSC and CG based ME (MCS-CGME) has faster convergence and better performa...

Salimi, Hamid; Soltanshahi, Mohammad Ali; Hatami, Javad

2012-01-01T23:59:59.000Z

463

Controlling biological networks by time-delayed signals  

E-Print Network [OSTI]

oscillations may become more robust (Stricker et al. [2008]; Ugander [2008]). Similarly, in neural networks://www.cds.caltech.edu/~murray/papers/omm09-ptrs-a.html #12;2 G. Orosz, J. Moehlis and R. M. Murray speaking, besides varying the strength

Murray, Richard M.

464

DNA MICROARRAY DATA CLUSTERING USING GROWING SELF ORGANIZING NETWORKS  

E-Print Network [OSTI]

the cells in the two different conditions are extracted and labeled with different fluorescent dyes (e developed incremental, competitive and self-organizing neural networks (Growing Cell Structures and Growing is to compare gene expression levels in two different samples (e.g. healthy and diseased cells). RNA from

Koprinska, Irena

465

Hardware implementation of a Hamming network  

E-Print Network [OSTI]

technology yet, the analog version was restricted to fixed-analog values (voltages). Simulation results showed the feasibility of the proposed hardware implementation, and experi- mentally, all the basic circuits performed as expected. In conclusion... evolved as one of the most challenging, fascinating and promising fields of study. The long-term goal of neural network researchers is to implement a machine that can make its own decisions and learn from self-experience. A machine that could approach...

Robinson Gonzalez, Moise?s Emanuel

1991-01-01T23:59:59.000Z

466

7Neural Decoding for Motor and Communication  

E-Print Network [OSTI]

computer cursors and robotic arms to various target locations simply by activating neural populations and Communication Prostheses Occipital Temporal Frontal Parietal PO sprd Lu IO sT L Pr iPd spcd OT EC FrO C iA sA sp

Yu, Byron

467

Network Programming 1 Computer Networks  

E-Print Network [OSTI]

Network Programming 1 Computer Networks client/server architecture, protocols, and sockets 2;Network Programming 1 Computer Networks client/server architecture, protocols, and sockets 2 Network;client/server architecture Client/server architecture defines the communication between two computers

Verschelde, Jan

468

Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine  

SciTech Connect (OSTI)

Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

Ondrej Linda; Milos Manic; Timothy R. McJunkin

2011-08-01T23:59:59.000Z

469

Development of a neural net paradigm that predicts simulator sickness  

SciTech Connect (OSTI)

A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.

Allgood, G.O.

1993-03-01T23:59:59.000Z

470

Seismic active control by neutral networks  

SciTech Connect (OSTI)

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

Tang, Yu

1995-12-31T23:59:59.000Z

471

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification  

SciTech Connect (OSTI)

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen [Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)] [Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

2014-05-15T23:59:59.000Z

472

Artificial Neural Nets and Cylinder Pressures in Diesel  

E-Print Network [OSTI]

Artificial Neural Nets and Cylinder Pressures in Diesel Engine Fault Diagnosis * Gopi O diagnosis system for a diesel engine, which uses artificial neural nets to identify faults on the basis­temporal representation of cylinder pressures. Draw cards and power cards are regularly assessed for the condition

Sharkey, Amanda

473

Optical Neural Interfaces Melissa R. Warden,1,2  

E-Print Network [OSTI]

Optical Neural Interfaces Melissa R. Warden,1,2 Jessica A. Cardin,5,6 and Karl Deisseroth2,3,4 1 Genetically encoded optical actuators and indicators have changed the land- scape of neuroscience, enabling review the development of optical neural interfaces, focusing on hardware designed for optical control

Deisseroth, Karl

474

Principles of Experience-Dependent Neural Plasticity: Implications for  

E-Print Network [OSTI]

Principles of Experience-Dependent Neural Plasticity: Implications for Rehabilitation After Brain and considerations in applying them to the damaged brain. Method: Neuroscience research using a variety of models. Results: Neural plasticity is believed to be the basis for both learning in the intact brain

Jones, Theresa A.

475

The Brain, the Broken Brain & the Neural Biology of Language  

E-Print Network [OSTI]

The Brain, the Broken Brain & the Neural Biology of Language Presenter: Steven Small Time & Date.maproom.com )1949 N. Hoyne Café Email list https://cfcpwork.uchicago.edu/mailman/listinfo/cafe #12;The Brain, the Broken Brain & the Neural Biology of Language Presenter: Steven Small Time & Date: 7-9 PM Monday

Collar, Juan I.

476

E-Print Network 3.0 - autoassociative neural network Sample Search...  

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

Technologies and Information Sciences 8 Communicated by Garrison Cottrell Dimension Reduction by Local Principal Component Analysis Summary: (PCA), researchers in the...

477

Bayesian Neural Networks and Density Networks David J.C. MacKay  

E-Print Network [OSTI]

by multiplying the 1 #12; ­10 ­5 0 5 10 ­10 ­5 0 5 10 0.5 1 x1 x2 a1) w = (0; 2) ­10 ­5 0 5 10 ­10 ­5 0 5 10 0

MacKay, David J.C.

478

Frontiers in Neural Circuits www.frontiersin.org February 2009 | Volume 3 | Article 1 | 1 NEURAL CIRCUITS  

E-Print Network [OSTI]

Frontiers in Neural Circuits www.frontiersin.org February 2009 | Volume 3 | Article 1 | 1 NEURAL components: synaptic transmis- sion,dendritic integration of synaptic events,the spike generating mechanism generating mechanism in cortical cells has been shown to react reliable and with high temporal preci- sion

479

Neural PID Control of Robot Manipulators with Application to an Upper Limb Exoskeleton  

E-Print Network [OSTI]

1 Neural PID Control of Robot Manipulators with Application to an Upper Limb Exoskeleton Wen Yu to uncertainties in robot control, PID control needs a big integral gain, or a neural compensator is added of the robot control. In this paper, we extend the popular neural PD control into neural PID control

Rosen, Jacob

480

Predictive Modeling of Mercury Speciation in Combustion Flue Gases Using GMDH-Based Abductive Networks  

E-Print Network [OSTI]

to develop. The use of modern data-based machine learning techniques has been recently introduced, including and boiler operating conditions. Prediction performance compares favourably with neural network models for future work to further improve performance. Index Terms: Mercury speciation, Flue gases, Boiler emissions

Abdel-Aal, Radwan E.

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

Transient Mixed Synapses Regulate Emerging Connectivity in Simple Neuronal Networks  

E-Print Network [OSTI]

separate inputs, use electrical coupling as a coincident detector to produce different electrical outputs depending on the temporal sequence of the incoming signals (Veruki and Hartveit, 2002). Coincident subthreshold inputs can summate due to electrical... creation of cytosolic continuity between the cells. Compartmentalization of neural networks within the nervous system can be useful in augmenting the outputs of those circuits. In this instance, coupled neurons that receive separate inputs, but have...

Richardson, Jarret Keith

2013-07-29T23:59:59.000Z

482

Cognitive Radio Networks as Sensor Networks  

E-Print Network [OSTI]

is used, assuming the cognitive radios know their ownfor Embedded Networked Sensing Cognitive Radio Networks AsJ. Pottie Introduction: Cognitive Radio (CR) Networks The

Bandari, Dorna; Yang, Seung R.; Zhao, Yue; Pottie, Gregory

2007-01-01T23:59:59.000Z

483

If we consider the differences in the list of topics treated in the two books then we see for example that Ripley treats some nonneural techniques such as belief networks and decision trees  

E-Print Network [OSTI]

If we consider the differences in the list of topics treated in the two books then we see for example that Ripley treats some nonneural techniques such as belief networks and decision trees while general book on pattern recognition, treating neural networks as one of the topics. Bishop's book can

Duin, Robert P.W.

484

Using neural population decoding to understand high level visual processing  

E-Print Network [OSTI]

The field of neuroscience has the potential to address profound questions including explaining how neural activity enables complex behaviors and conscious experience. However, currently the field is a long way from ...

Meyers, Ethan M

2011-01-01T23:59:59.000Z

485

A system for efficient neural stimulation with energy recovery  

E-Print Network [OSTI]

An analog VLSI-based low-power neural tissue stimulator is presented as a part of the MIT and Massachusetts Eye and Ear Infirmary Retinal Implant Project to develop a prosthesis for restoring some useful vision to patients ...

Kelly, Shawn Kevin, 1973-

2004-01-01T23:59:59.000Z

486

Linking dopaminergic physiology to working memory related neural circuitry  

E-Print Network [OSTI]

Working memory is the ability to hold information "online" over a delay in order to perform a task. This kind of memory is thought to be encoded in the brain by persistent neural activity that outlasts the presentation of ...

Bolton, Andrew D. (Andrew Donald)

2014-01-01T23:59:59.000Z

487

Two Neural Correlates of Consciousness1 New York University  

E-Print Network [OSTI]

1 Two Neural Correlates of Consciousness1 Ned Block New York University Departments of Philosophy events and mechanisms jointly sufficient for a specific conscious percept" [9, p. 16] . However, since

Block, Ned

488

Imaging neural correlates of syntactic complexity in a naturalistic context  

E-Print Network [OSTI]

The aim of this thesis, and the research project within which it is embedded, is to delineate a neural model of grammatical competence. For this purpose, we develop here a novel integrated, multi-disciplinary experimental ...

Bachrach, Asaf

2008-01-01T23:59:59.000Z

489

Parylene Coated Silicon Probes for Neural Prosthesis Ray Huang1*  

E-Print Network [OSTI]

breakage. However, manufacturing limitations have prevented a strong and biocompatible silicon electrode as well as the in vitro electrical characterization of the gold and platinum micro electrodes. Keywords - parylene cable; neural prosthesis; silicon probe I. INTRODUCTION An important

Andersen, Richard

490

Network Perspectives on Communities  

E-Print Network [OSTI]

W W. Networks Fields and Organizations: Micro-Dynamics Scaleentitled ďNetworks, Fields and Organizations: Micro-

Wolfe, Alvin W

2006-01-01T23:59:59.000Z

491

Network: Computation in Neural Systems, 9, 419-432. The influence of neural activity and intracortical connections on  

E-Print Network [OSTI]

observed in several other models (e.g. Sirosh & Miikkulainen (1997)). Recent data has also suggested

Goodhill, Geoffrey J.

492

Routing in hybrid networks  

E-Print Network [OSTI]

Hybrid networks are networks that have wired as well as wireless components. Several routing protocols exist for traditional wired networks and mobile ad-hoc networks. However, there are very few routing protocols designed for hybrid networks...

Gupta, Avinash

2001-01-01T23:59:59.000Z

493

Perturbation centrality: a novel centrality measure obtained by the general network dynamics tool, Turbine  

E-Print Network [OSTI]

Analysis of network dynamics became increasingly important to understand the mechanisms and consequences of changes in biological systems from macromolecules to cells and organisms. Currently available network dynamics tools are mostly tailored for specific tasks such as calculation of molecular or neural dynamics. Our Turbine software offers a generic framework enabling the simulation of any algorithmically definable dynamics of any network. Turbine is also optimized for handling very large networks in the range of millions of nodes and edges. Using a perturbation transmission model inspired by communicating vessels, here we introduce a novel centrality measure termed as perturbation centrality. Perturbation centrality is the reciprocal of the time needed to dissipate a starting perturbation in the network. Hubs and inter-modular nodes proved to be highly efficient in perturbation propagation. High perturbation centrality nodes of the Met-tRNA synthetase protein structure network were identified as amino aci...

Szalay, Kristof Z

2013-01-01T23:59:59.000Z

494

Facilitatory neural dynamics for predictive extrapolation  

E-Print Network [OSTI]

a wiggly trajectory compared to FAN. (c), (f) FAN shows a smooth trajectory with a very small footprint. . . . . . . . . . . . 80 xxiv FIGURE Page 45 Success rate under different delay conditions. The success rates for balancing the pole using... under delay condition shows high success rate in perform- ing blank-out test and shows slow decrease in performance until 60 steps (600 ms), which is similar to the observation in human experiments by [74]. (b) Compared to the control network, FAN learns...

Lim, Hee Jin

2009-06-02T23:59:59.000Z

495

Microfluidic Integration into Neural Implants University of Southern California, Los Angeles, CA  

E-Print Network [OSTI]

Microfluidic Integration into Neural Implants E. Meng1 1 University of Southern California, Los technological deficiencies can be addressed by integrating microfluidics with electrodes and electrochemical sensors. Multimodality neural interfaces that combine electronics and microfluidics open new possibilities

Meng, Ellis

496

Neural stem cell differentiation in collagen scaffolds for retinal tissue engineering  

E-Print Network [OSTI]

Rat neural stem cells (NSCs) were cultured in monolayer or in porous collagen scaffolds and exposed to neurogenic or non-neurogenic medium to determine the effects on neural differentiation and neurite growth. Nestin, ...

Ueda, Erica (Erica Ann)

2008-01-01T23:59:59.000Z

497

Integrating Neuromuscular and Cyber Systems for Neural Control of Artificial Legs  

E-Print Network [OSTI]

and computer system is essential. This integration leads to a cyber- physical system (CPS), in which a complex (CPS) for neurally controlled artificial legs. The key to the new CPS system is the neural

Yang, Qing "Ken"

498

Unlocking the brain's mysteries: Meet the bioengineers behind next-generation neural devices  

ScienceCinema (OSTI)

Bioengineers in the Neural Technologies Group at Lawrence Livermore are creating the next generation of clinical- and research-quality neural interfaces. The goal is to gain a fundamental understanding of neuroscience, treat a variety of debilitating neurological disorders (such as Parkinson's, depression, and epilepsy), and restore lost neural functions such as sight, hearing, and mobility.

Pannu, Sat; Shah, Kedar; Tolosa, Vanessa; Tooker, Angela

2015-02-20T23:59:59.000Z

499

Unlocking the brain's mysteries: Meet the bioengineers behind next-generation neural devices  

SciTech Connect (OSTI)

Bioengineers in the Neural Technologies Group at Lawrence Livermore are creating the next generation of clinical- and research-quality neural interfaces. The goal is to gain a fundamental understanding of neuroscience, treat a variety of debilitating neurological disorders (such as Parkinson's, depression, and epilepsy), and restore lost neural functions such as sight, hearing, and mobility.

Pannu, Sat; Shah, Kedar; Tolosa, Vanessa; Tooker, Angela

2014-10-02T23:59:59.000Z

500

Unsupervised Learning in Networks of Spiking Neurons Using Temporal Coding  

E-Print Network [OSTI]

. We propose a mechanism for unsupervised learning in networks of spiking neurons which is based on the timing of single firing events. Our results show that a topology preserving behaviour quite similar to that of Kohonen's self-organizing map can be achieved using temporal coding. In contrast to previous approaches, which use rate coding, the winner among competing neurons can be determined fast and locally. Hence our model is a further step towards a more realistic description of unsupervised learning in biological neural systems. 1 Introduction In the area of modelling information processing in biological neural systems, there is an ongoing debate about which essentials have to be taken into account (see e.g. [3,13,11,9]). Discrete models, such as threshold gates or McCullochPitts neurons, are undoubtedly very simplistic descriptions of biological neurons. Models with real-valued output, such as the sigmoidal gate, where analogue values are interpreted as firing rates of biologica...

1997-01-01T23:59:59.000Z