Sample records for back-propagation neural network

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

    E-Print Network [OSTI]

    Bullinaria, John

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

  2. Handwritten Digit Recognition with a Back-Propagation Network

    E-Print Network [OSTI]

    Parker, Gary B.

    Handwritten Digit Recognition with a Back-Propagation Network Y. Le Cun, B. Boser, J. S. Denker, D We present an application of back-propagation networks to hand- written digit recognition. Minimal. 1 INTRODUCTION The main point of this paper is to show that large back-propagation (BP) net- works

  3. Handwritten Digit Recognition with a BackPropagation Network

    E-Print Network [OSTI]

    LeCun, Yann

    Handwritten Digit Recognition with a Back­Propagation Network Y. Le Cun, B. Boser, J. S. Denker, D We present an application of back­propagation networks to hand­ written digit recognition. Minimal. 1 INTRODUCTION The main point of this paper is to show that large back­propagation (BP) net­ works

  4. Contextual Back-Propagation Technical Report UT-CS-00-443

    E-Print Network [OSTI]

    MacLennan, Bruce

    Contextual Back-Propagation Technical Report UT-CS-00-443 Bruce J. MacLennan #3; Computer Science. This report presents an adaptation of the back- propagation algorithm to training contextual neural networks and adaptation must also be context- dependent. The basic idea is simple enough | hold the context constant while

  5. An Empirical Study of Learning Speed in BackPropagation Networks

    E-Print Network [OSTI]

    Fahlman, Scott E.

    the basic ideas of connectionism or back­propagation learning. See [3] for a brief overview of this areaAn Empirical Study of Learning Speed in Back­Propagation Networks Scott E. Fahlman September 1988 of the back­propagation algorithm. However, back­propagation learning is too slow for many applications

  6. Image Compression by Back Propagation

    E-Print Network [OSTI]

    Cottrell, Garrison W.

    CHAPTER 9 Image Compression by Back Propagation: An Example of Extensional Programming* GARRISON W the case with the computatiolls associated with basic cognitive pro- cesses such as vision and audition techniques. The technique we employ is known as back propagation. developed by l1umelhart, Hinton

  7. Training a 3-Node Neural Network is NP-Complete

    E-Print Network [OSTI]

    Rivest, Ronald L.

    . A good discussion of the theory of NP-completeness, as well as a description of several hundreds of NPTraining a 3-Node Neural Network is NP-Complete Avrim L. Blum and Ronald L. Rivest* MIT Laboratory-propagation algorithm promises just that. In practice, however, the back-propagation algorithm often runs very slowly

  8. Automatic real-time lip synchronization using LPC analysis and neural networks

    E-Print Network [OSTI]

    Haaser, Christina Marie

    2002-01-01T23:59:59.000Z

    , specifically LPC cepstral coefficients, gain, and zero-crossing rate. These features are used as input into a trained three-layer feedforward back-propagation neural network for phonetic classification per frame of animation. The training of the neural network...

  9. Computationally Efficient Neural Network Intrusion Security Awareness

    SciTech Connect (OSTI)

    Todd Vollmer; Milos Manic

    2009-08-01T23:59:59.000Z

    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.

  10. Seismic active control by neural networks.

    SciTech Connect (OSTI)

    Tang, Y.

    1998-01-01T23:59:59.000Z

    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.

  11. Artificial Neural Networks Single Layer Networks Multi Layer Networks Generalization Artificial Neural Networks

    E-Print Network [OSTI]

    Kjellstrm, Hedvig

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

  12. Artificial Neural Networks Single Layer Networks Multi Layer Networks Generalization Artificial Neural Networks

    E-Print Network [OSTI]

    Kjellstrm, Hedvig

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

  13. Analysis of neutron noise spectra using neural networks

    SciTech Connect (OSTI)

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

    1991-01-01T23:59:59.000Z

    Neural network architectures based on the back-propagation paradigm have been developed to recognize the features, and detect resonance shifts in, power spectral density (PSD) data. Our goal is to advance the state of the art in the application of noise analysis techniques to monitor nuclear reactor internals. The initial objectives have been to use PSD data, acquired over a period of about 2 years by PSDREC (power spectral density recognition system), to develop neural networks that are able to differentiate between normal neutron power spectral density data and anomalous spectral data, and detect significant shifts in the positions of spectral resonances while reducing the effect of small shifts. Neural network systems referred to in this paper as spectral feature detectors (SFDs) and integral network filters have been developed to meet these objectives. The performance of the SFDs is the subject of this paper. 2 refs., 2 figs.

  14. Using Neural Networks

    E-Print Network [OSTI]

    Gabel, S.

    2003-01-01T23:59:59.000Z

    A neural network approach is employed for estimating key efficiency parameters in a gas turbine engine. The concept is demonstrated within a limited operating region for a given engine. The neural network is developed to estimate certain...

  15. Using Neural Networks

    E-Print Network [OSTI]

    Gabel, S.

    A neural network approach is employed for estimating key efficiency parameters in a gas turbine engine. The concept is demonstrated within a limited operating region for a given engine. The neural network is developed to estimate certain...

  16. 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

  17. 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]

    Jeffery, Simon

    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

  18. Foundations of Artificial Intelligence Neural Networks

    E-Print Network [OSTI]

    Qu, Rong

    Foundations of Artificial Intelligence Neural Networks Building Artificial Brains #12;Background Session 2 Software Demonstrations Real World Applications #12;Artificial Neural Networks ... consists to operate. Wiki #12;Relationship between Artificial Neural Networks & the Human Brain Neural networks

  19. Neural Network Based Intrusion Detection System for Critical Infrastructures

    SciTech Connect (OSTI)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01T23:59:59.000Z

    Resiliency and security in control systems such as SCADA and Nuclear plants in todays world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  20. Back Propagation is Sensitive to Initial Conditions John F. Kolen

    E-Print Network [OSTI]

    Pollack, Jordan B.

    Back Propagation is Sensitive to Initial Conditions John F. Kolen Jordan B. Pollack Laboratory Columbus, Ohio 43210, USA kolenj@cis.ohiostate.edu, pollack@cis.ohiostate.edu TR 90JKBPSIC ABSTRACT. Kolen Jordan B. Pollack Laboratory for Artificial Intelligence Research Computer and Information Science

  1. Back Propagation is Sensitive to Initial Conditions John F. Kolen

    E-Print Network [OSTI]

    Pollack, Jordan B.

    Back Propagation is Sensitive to Initial Conditions John F. Kolen Jordan B. Pollack Laboratory Columbus, Ohio 43210, USA kolen-j@cis.ohio-state.edu, pollack@cis.ohio-state.edu TR 90-JK-BPSIC ABSTRACT. Kolen Jordan B. Pollack Laboratory for Artificial Intelligence Research Computer and Information Science

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

    SciTech Connect (OSTI)

    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

    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.

  3. Aircraft System Identification Using Artificial Neural Networks

    E-Print Network [OSTI]

    Valasek, John

    Aircraft System Identification Using Artificial Neural Networks Kenton Kirkpatrick Jim May Jr. John Meeting January 9, 2013 Compos Volatus #12;Overview Motivation System Identification Artificial Neural Networks 2 Artificial Neural Networks ANNSID Conclusions and Open Challenges #12;Motivation 3 #12

  4. A VALIDATION INDEX FOR ARTIFICIAL NEURAL NETWORKS

    E-Print Network [OSTI]

    Roberts, Stephen

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

  5. autoassociative 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 98 Fuzzy neural network pattern recognition algorithm for classification of the events in...

  6. 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...

  7. 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...

  8. 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...

  9. Calibrating Artificial Neural Networks by Global Optimization

    E-Print Network [OSTI]

    Janos D. Pinter

    2010-07-21T23:59:59.000Z

    Jul 21, 2010 ... Abstract: An artificial neural network (ANN) is a computational model ... emulating the key features and operations of biological neural networks.

  10. Semiring Artificial Neural Networks and Weighted Automata

    E-Print Network [OSTI]

    Hoelldobler, Steffen

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

  11. Introduction to Artificial Intelligence Neural Networks

    E-Print Network [OSTI]

    Qu, Rong

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

  12. Artificial Neural Network Portion of Coil Study

    E-Print Network [OSTI]

    Putten, Peter van der

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

  13. Artificial Bee Colony Training of Neural Networks

    E-Print Network [OSTI]

    Bullinaria, John

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

  14. 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...

  15. 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...

  16. 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...

  17. 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...

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

    E-Print Network [OSTI]

    Keeni, Kanad; Nakayama, Kenji; Shimodaira, Hiroshi

    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 ...

  19. Object Oriented Artificial Neural Network Implementations

    E-Print Network [OSTI]

    Slatton, Clint

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

  20. Online learning processes artificial neural networks

    E-Print Network [OSTI]

    Heskes, Tom

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

  1. Neural Networks Perceptrons and Backpropagation

    E-Print Network [OSTI]

    Bremen, Universität

    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

  2. 1 Introduction The artificial neural network discussed in this paper is unlike most other neural network

    E-Print Network [OSTI]

    Delaware, University of

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

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

    SciTech Connect (OSTI)

    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

    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.

  4. Neural networks for fast image compression

    E-Print Network [OSTI]

    Li, Mu

    1998-01-01T23:59:59.000Z

    of one network have been used in the system. Each neural network was trained with some image blocks which have similar characteristics. In order to decrease the time for the learning process of the neural networks to converge, an adaptive back...

  5. Algorithms and Hardware for Implementing Artificial Neural Networks Nathan Hower

    E-Print Network [OSTI]

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

  6. Nonlinear adaptive internal model control using neural networks

    E-Print Network [OSTI]

    Gandhi, Amit Krushnavadan

    2001-01-01T23:59:59.000Z

    (NIMC) strategy based on neural network models is presented for SISO processes. The nonlinearities of the dynamical system are modelled by neural network architectures. Recurrent neural networks can be used for both the identification and control of nonlinear...

  7. Neural network based system for equipment surveillance

    DOE Patents [OSTI]

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

    1998-01-01T23:59:59.000Z

    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.

  8. Neural network based system for equipment surveillance

    DOE Patents [OSTI]

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

    1998-04-28T23:59:59.000Z

    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.

  9. Artificial neural networks in models of specialization, guild evolution

    E-Print Network [OSTI]

    Getz, Wayne M.

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

  10. 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...

  11. 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...

  12. Tea classification based on artificial olfaction using bionic olfactory neural network

    E-Print Network [OSTI]

    Yang, X L; Fu, J; Lou, Z G; Wang, L Y; Li, G; Freeman, Walter J III

    2006-01-01T23:59:59.000Z

    conventional artificial neural network (ANN), chaos shoulda con- ventional artificial neural network, BP network, isconventional artificial neural network, it is an accurate

  13. Large margin classification in infinite neural networks

    E-Print Network [OSTI]

    Saul, Lawrence K.

    Large margin classification in infinite neural networks Youngmin Cho and Lawrence K. Saul, CA 92093-0404 Abstract We introduce a new family of positive-definite kernels for large margin classi- fication in support vector machines (SVMs). These kernels mimic the computation in large neural networks

  14. The neural network approach to parton distributions

    E-Print Network [OSTI]

    Andrea Piccione; Joan Rojo; for the NNPDF Collaboration

    2005-10-05T23:59:59.000Z

    We introduce the neural network approach to global fits of parton distrubution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.

  15. Neural network approach to parton distributions fitting

    E-Print Network [OSTI]

    Andrea Piccione; Joan Rojo; for the NNPDF Collaboration

    2005-10-18T23:59:59.000Z

    We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on each Monte Carlo replica via a genetic algorithm. Results on the proton and deuteron structure functions, and on the nonsinglet parton distribution will be shown.

  16. aco-bp 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 87 Fuzzy neural network pattern recognition algorithm for classification of the events in...

  17. Fundamental building blocks for a compact optoelectronic neural network processor

    E-Print Network [OSTI]

    Ruedlinger, Benjamin Franklin, 1976-

    2003-01-01T23:59:59.000Z

    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 ...

  18. Enhancing neural-network performance via assortativity

    SciTech Connect (OSTI)

    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

    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.

  19. Reviews of computing technology: An overview of neural networks

    SciTech Connect (OSTI)

    Rainsford, A.E.

    1992-02-15T23:59:59.000Z

    This report discusses the historical background, models, computer hardware, and uses of neural networks. (LSP)

  20. 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...

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. Nonlinear programming with feedforward neural networks.

    SciTech Connect (OSTI)

    Reifman, J.

    1999-06-02T23:59:59.000Z

    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.

  6. Prediction of Burr Formation during Face Milling Using an Artificial Neural Network with Optimized Cutting Conditions

    E-Print Network [OSTI]

    Lee, S H; Dornfeld, D A

    2007-01-01T23:59:59.000Z

    Appli- cation of artificial neural network in laser weldingwith minimal heights. Artificial neural network and non-milling using an artificial neural network with optimized

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

    E-Print Network [OSTI]

    Dyer, Bill

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

  8. Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate

    E-Print Network [OSTI]

    Yang, Qingsheng; Mwenda, Kevin M; Ge, Miao

    2013-01-01T23:59:59.000Z

    reasoning and artificial neural network techniques forfactors with artificial neural networks to predict referencefactors with artificial neural networks to predict reference

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

    E-Print Network [OSTI]

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

    2014-01-01T23:59:59.000Z

    simulation and artificial neural network for forecastingloads using artificial neural networks, 2001 World Congress,consumption by using artificial neural network, Advances in

  10. Aircraft System Identification Using Artificial Neural Networks

    E-Print Network [OSTI]

    Valasek, John

    Aircraft System Identification Using Artificial Neural Networks Kenton Kirkpatrick , Jim May Jr linear system identification for aircraft using artificial neural net- works. The output of a linear the correct model. In this paper, a new method of system identification is proposed that uses artificial

  11. 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...

  12. 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...

  13. 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...

  14. Tampa Electric Neural Network Sootblowing

    SciTech Connect (OSTI)

    Mark A. Rhode

    2004-03-31T23:59:59.000Z

    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.

  15. Using neural networks to locate pitch accents.

    E-Print Network [OSTI]

    Taylor, Paul A

    1995-01-01T23:59:59.000Z

    This paper descirbes a technique for finding intonatioanl events, (pitch accents and boundary tones) from waveforms. The technique works in a bottom-up manner by using a recurrent neural network to perform a classification of ...

  16. Imbibition well stimulation via neural network design

    DOE Patents [OSTI]

    Weiss, William (Socorro, NM)

    2007-08-14T23:59:59.000Z

    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.

  17. Automatic well log correlation using neural networks

    E-Print Network [OSTI]

    Habiballah, Walid Abdulrahim

    1991-01-01T23:59:59.000Z

    AUTOMATIC WELL LOG CORRELATION USING NEURAL NETWORKS A Thesis by WALID ABDULHAHIM HABIBALLAH Submitted to the Office of Graduate Studies of Texas AaM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE... August 1991 Major Subject; Petroleum Engineering AUTOMATIC WELL LOG CORRELATION USING NEURAL NETWORKS A Thesis by WALID ABDULRAHIM HABIBALLAH Approved as to style and content by: R. A. St tzman (Chair of Committee) S. W. Poston (Member) R. R...

  18. 1368 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 8, AUGUST 2009 Simple Artificial Neural Networks That Match Probability

    E-Print Network [OSTI]

    Dawson, Michael

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

  19. artificial neural networks-particle: 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 69 Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks Computer Technologies...

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

    E-Print Network [OSTI]

    Sarkar, Dilip

    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

  1. Parametric Optimization of Artificial Neural Networks for Signal Approximation Applications

    E-Print Network [OSTI]

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

  2. Modeling of a continuous food process with neural networks

    E-Print Network [OSTI]

    Bullock, David Cole

    1995-01-01T23:59:59.000Z

    Three neural networks were constructed and trained to provide both next step prediction and multi-step prediction of a snack food continuous frying operation. The three neural models were a feedforward sigmoidal network ...

  3. Characterization of Shape Memory Alloys Using Artificial Neural Networks

    E-Print Network [OSTI]

    Valasek, John

    1 Characterization of Shape Memory Alloys Using Artificial Neural Networks Jim Henrickson, Kenton Shape Memory Alloys Artificial Neural Networks Process Implement Shape Memory Alloy Model;3 Introduction Shape memory alloys (SMAs) Active material: material that undergoes macroscopic change

  4. Modeling of a continuous food process with neural networks

    E-Print Network [OSTI]

    Bullock, David Cole

    1995-01-01T23:59:59.000Z

    Three neural networks were constructed and trained to provide both next step prediction and multi-step prediction of a snack food continuous frying operation. The three neural models were a feedforward sigmoidal network (FFN), a radial basis...

  5. Learning in a hierarchical neural network

    E-Print Network [OSTI]

    Michaelis, Matthew Clinton

    1986-01-01T23:59:59.000Z

    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...) ~ excita tory syn apse ? 0 inhibitory synapse ~ excitatory nonmodifiable synapse inhibitory nonmodifiable synapse Fig. 2. Connections Between Layers in the Network 12 in layer C ? 1. Note that the excitatory and the inhibitory connections...

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

    E-Print Network [OSTI]

    Joy, Mike

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

  7. Safety Lifecycle for Developing Safety Critical Artificial Neural Networks

    E-Print Network [OSTI]

    Kelly, Tim

    Safety Lifecycle for Developing Safety Critical Artificial Neural Networks Zeshan Kurd, Tim Kelly.kelly}@cs.york.ac.uk Abstract. Artificial neural networks are employed in many areas of industry such as medicine and defence a safety lifecycle for artificial neural networks. The lifecycle fo- cuses on managing behaviour

  8. Combinatorial Optimization with Feedback Artificial Neural Networks \\Lambda

    E-Print Network [OSTI]

    Peterson, Carsten

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

  9. Extracting Provably Correct Rules from Artificial Neural Networks

    E-Print Network [OSTI]

    Clausen, Michael

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

  10. Novel Artificial Neural Networks For Remote-Sensing Data Classification

    E-Print Network [OSTI]

    Michel, Howard E.

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

  11. Using Artificial Neural Networks to Play Pong Luis E. Ramirez

    E-Print Network [OSTI]

    Meeden, Lisa A.

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

  12. Devices and Circuits for Nanoelectronic Implementation of Artificial Neural Networks

    E-Print Network [OSTI]

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

  13. Safety Criteria and Safety Lifecycle for Artificial Neural Networks

    E-Print Network [OSTI]

    Kelly, Tim

    Safety Criteria and Safety Lifecycle for Artificial Neural Networks Zeshan Kurd, Tim Kelly and Jim. The paper also presents a safety lifecycle for artificial neural networks. This lifecycle focuses, knowledge. INTRODUCTION Artificial neural networks (ANNs) are used in many safety-related applications

  14. Artificial Neural Networks for Recognition of Electrocardiographic Lead Reversal

    E-Print Network [OSTI]

    Peterson, Carsten

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

  15. REGULARIZATION OF A PROGRAMMED RECURRENT ARTIFICIAL NEURAL NETWORK

    E-Print Network [OSTI]

    Meade, Andrew J.

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

  16. A NOVEL MAP PROJECTION USING AN ARTIFICIAL NEURAL NETWORK

    E-Print Network [OSTI]

    Skupin, Andr

    A NOVEL MAP PROJECTION USING AN ARTIFICIAL NEURAL NETWORK Andr Skupin Department of Geography is an unsupervised, artificial neural network (ANN) technique. While it should not be mistaken as imitating neural network approach in two ways. First, the paper presents a cartographically informed method

  17. Chaotic time series prediction using artificial neural networks

    SciTech Connect (OSTI)

    Bartlett, E.B.

    1991-12-31T23:59:59.000Z

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  18. Chaotic time series prediction using artificial neural networks

    SciTech Connect (OSTI)

    Bartlett, E.B.

    1991-01-01T23:59:59.000Z

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  19. An introduction to artificial neural networks

    E-Print Network [OSTI]

    C. A. L. Bailer-Jones; R. Gupta; H. P. Singh

    2001-02-13T23:59:59.000Z

    Artificial neural networks are algorithms which have been developed to tackle a range of computational problems. These range from modelling brain function to making predictions of time-dependent phenomena to solving hard (NP-complete) problems. In this introduction we describe a single, yet very important, type of network known as a feedforward network. This network is a mathematical model which can be trained to learn an arbitrarily complex relationship between a data and a parameter domain, so can be used to solve interpolation and classification problems. We discuss the structure, training and interpretation of these networks, and their implementation, taking the classification of stellar spectra as an example.

  20. Automated Interpretation of Myocardial SPECT Perfusion Images Using Artificial Neural Networks

    E-Print Network [OSTI]

    Peterson, Carsten

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

  1. Auto-associative nanoelectronic neural network

    SciTech Connect (OSTI)

    Nogueira, C. P. S. M.; Guimares, J. G. [Departamento de Engenharia Eltrica - Laboratrio de Dispositivos e Circuito Integrado, Universidade de Braslia, CP 4386, CEP 70904-970 Braslia DF (Brazil)

    2014-05-15T23:59:59.000Z

    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.

  2. Optimization and integration of renewable energy sources on a community scale using Artificial Neural Networks and Genetic Algorithms

    E-Print Network [OSTI]

    Davis, Bron

    2011-01-01T23:59:59.000Z

    algorithm, and Artificial Neural Network." Building andOrtega. "New artificial neural network prediction method fora feedback artificial neural network." Energy and Buildings

  3. Applications of artificial neural networks predicting macroinvertebrates in freshwaters

    E-Print Network [OSTI]

    Lek, Sovan

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

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

    E-Print Network [OSTI]

    Antsaklis, Panos

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

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

    E-Print Network [OSTI]

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

  6. Recognizing targets from infrared intensity scan patterns using artificial neural networks

    E-Print Network [OSTI]

    Barshan, Billur

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

  7. A RECONFIGURABLE COMPUTING ARCHITECTURE FOR IMPLEMENTING ARTIFICIAL NEURAL NETWORKS ON FPGA

    E-Print Network [OSTI]

    Areibi, Shawki M

    A RECONFIGURABLE COMPUTING ARCHITECTURE FOR IMPLEMENTING ARTIFICIAL NEURAL NETWORKS ON FPGA Professor Shawki Areibi Artificial Neural Networks (ANNs), and the backpropagation algorithm in particular NEURAL NETWORKS ON FPGA Kristian Nichols University of Guelph, 2003 Advisor: Professor Medhat Moussa

  8. Neural networks as nonlinear models in Air Force personnel analysis: a prospectus and exploratory results

    E-Print Network [OSTI]

    Wiggins, Vince L.

    1996-01-01T23:59:59.000Z

    This research evaluates the potential for applying neural networks to Air Force personnel analysis through a review of relevant literature and empirical testing of neural networks in the domain of personnel research. Neural network technology has...

  9. The neural network approach to parton distribution functions

    E-Print Network [OSTI]

    Joan Rojo

    2006-07-11T23:59:59.000Z

    We introduce the neural network approach to the parametrization of parton distributions. After a general introduction, we present in detail our approach to parametrize experimental data, based on a combination of Monte Carlo methods and neural networks. We apply this strategy first in three different cases: the proton structure function, hadronic tau decays and B meson decay spectra. Finally we describe the neural network approach applied to the parametrization of parton distribution functions, and present results on the nonsinglet parton distribution.

  10. Artificial neural network cardiopulmonary modeling and diagnosis

    DOE Patents [OSTI]

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

    1997-01-01T23:59:59.000Z

    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.

  11. Artificial neural network cardiopulmonary modeling and diagnosis

    DOE Patents [OSTI]

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

    1997-10-28T23:59:59.000Z

    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.

  12. Analysis of complex systems using neural networks

    SciTech Connect (OSTI)

    Uhrig, R.E. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering]|[Oak Ridge National Lab., TN (United States)

    1992-12-31T23:59:59.000Z

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: (1) Diagnostics: State of the Plant (2) Hybrid System for Transient Identification, (3) Sensor Validation, (4) Plant-Wide Monitoring, (5) Monitoring of Performance and Efficiency, and (6) Analysis of Vibrations. Although specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems.

  13. Analysis of complex systems using neural networks

    SciTech Connect (OSTI)

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

    1992-01-01T23:59:59.000Z

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: (1) Diagnostics: State of the Plant (2) Hybrid System for Transient Identification, (3) Sensor Validation, (4) Plant-Wide Monitoring, (5) Monitoring of Performance and Efficiency, and (6) Analysis of Vibrations. Although specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems.

  14. Applications of Artificial Neural Networks (ANNs) to Rotating Equipment

    E-Print Network [OSTI]

    Sainudiin, Raazesh

    , engines), driven equipment (compressors, pumps, mixers, fans, extruders), transmission devices (gears diagnosis, trouble shooting, maintenance, sensor validation, and control. Artificial Neural Network (ANN

  15. Optimized Learning with Bounded Error for Feedforward Neural Networks

    E-Print Network [OSTI]

    Maggiore, Manfredi

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

  16. Neural networks and their application to nuclear power plant diagnosis

    SciTech Connect (OSTI)

    Reifman, J. [Argonne National Lab., IL (United States). Reactor Analysis Div.

    1997-10-01T23:59:59.000Z

    The authors present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal-hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for nuclear power plant operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-based diagnostic systems are identified. A total of thirty-four publications are reviewed.

  17. 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...

  18. abordagem neural para: 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 84 Deep...

  19. 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...

  20. 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...

  1. 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...

  2. Neural node network and model, and method of teaching same

    DOE Patents [OSTI]

    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

    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.

  3. Neural node network and model, and method of teaching same

    DOE Patents [OSTI]

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

    1995-12-26T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Getz, Wayne M.

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

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

    E-Print Network [OSTI]

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

    1999-12-19T23:59:59.000Z

    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.

  6. Artificial Neural Network Circuit for Spectral Pattern Recognition

    E-Print Network [OSTI]

    Rasheed, Farah

    2013-09-04T23:59:59.000Z

    Artificial Neural Networks (ANNs) are a massively parallel network of a large number of interconnected neurons similar to the structure of biological neurons in the human brain. ANNs find applications in a large number of fields, from pattern...

  7. Electric Power System Anomaly Detection Using Neural Networks

    E-Print Network [OSTI]

    Tronci, Enrico

    Electric Power System Anomaly Detection Using Neural Networks Marco Martinelli1 , Enrico Tronci1. The aim of this work is to propose an approach to monitor and protect Electric Power System by learning of an Electric Power System. In this paper, a neural network based approach for novelty detection is presented

  8. Developmental Plasticity in Cartesian Genetic Programming Artificial Neural Networks

    E-Print Network [OSTI]

    Fernandez, Thomas

    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

  9. Desynchronization in diluted neural networks

    SciTech Connect (OSTI)

    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

    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.

  10. Paraphrastic recurrent neural network language models

    E-Print Network [OSTI]

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

    2015-04-22T23:59:59.000Z

    segmentation transducer, T?:?? 1In common with other paraphrase induction methods [1, 18], this scheme can also produce phrase pairs that are non-paraphrastic, for exam- ple, antonyms. However, this is of less concern for language modelling, for which improving... models, Computer Speech & Language, vol. 21, no. 3, pp. 492518, 2007. [28] Y. Si, Q. Zhang, T. Li, J. Pan, and Y. Yan (2013), Prefix tree based n-best list re-scoring for recurrent neural network language model used in speech recognition system...

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

    E-Print Network [OSTI]

    West, Stuart

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

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

    E-Print Network [OSTI]

    Ezeife, Christie

    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

  13. Predicting Turbulence Using Partial Least Squares Regression and an Artificial Neural Network

    E-Print Network [OSTI]

    Lakshmanan, Valliappa

    Predicting Turbulence Using Partial Least Squares Regression and an Artificial Neural Network #12;Neural Network Neural Network Architecture 6 inputs (the 6 transformed components) 1 output (0 Lakshmanan et. al (OU/NSSL) PLS and NN 8th Conf. on AI, Atlanta, GA 9 / 15 #12;Neural Network Validation

  14. Constraint methods for neural networks and computer graphics

    SciTech Connect (OSTI)

    Platt, J.C.

    1989-01-01T23:59:59.000Z

    Both computer graphics and neural networks are related, in that they model natural phenomena. Physically-based models are used by computer graphics researchers to create realistic, natural animation, and neural models are used by neural network researchers to create new algorithms or new circuits. To exploit successfully these graphical and neural models, engineers want models that fulfill designer-specified goals. These goals are converted into mathematical constraints. This thesis presents constraint methods for computer graphics and neural networks. The mathematical constraint methods modify the differential equations that govern the neural or physically-based models. The constraint methods gradually enforce the constraints exactly. This thesis also described application of constrained models to real problems. The first half of this theses discusses constrained neural networks. The desired models and goals are often converted into constrained optimization problems. These optimization problems are solved using first-order differential equations. The applications of constrained neural networks include the creation of constrained circuits, error-correcting codes, symmetric edge detection for computer vision, and heuristics for the traveling salesman problem. The second half of this thesis discusses constrained computer graphics models. In computer graphics, the desired models and goals become constrained mechanical systems, which are typically simulated with second-order differential equations. The Penalty Method adds springs to the mechanical system to penalize violations of the constraints. Rate Controlled Constraints add forces and impulses to the mechanical system to fulfill the constraints with critically damped motion.

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

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

  16. Galaxies, Human Eyes and Artificial Neural Networks

    E-Print Network [OSTI]

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

    1994-12-08T23:59:59.000Z

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

  17. Estimating photometric redshifts with artificial neural networks

    E-Print Network [OSTI]

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

    2002-10-21T23:59:59.000Z

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

  18. 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...

  19. Neural networks in neuroscience: a brief overview Samuel Johnson1

    E-Print Network [OSTI]

    Johnson, Samuel

    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

  20. Digital neural network-based modeling technique for extrusion processes

    E-Print Network [OSTI]

    Jang, Won-Hyouk

    2001-01-01T23:59:59.000Z

    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...

  1. Neural network calibration for miniature multi-hole pressure probes

    E-Print Network [OSTI]

    Vijayagopal, Rajesh

    1998-01-01T23:59:59.000Z

    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...

  2. Monte Carlo event reconstruction implemented with artificial neural networks

    E-Print Network [OSTI]

    Tolley, Emma Elizabeth

    2011-01-01T23:59:59.000Z

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

  3. Model building in neural networks with hidden Markov models

    E-Print Network [OSTI]

    Wynne-Jones, Michael

    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 ...

  4. Evolution of Memory in Reactive Artificial Neural Networks

    E-Print Network [OSTI]

    Chung, Ji Ryang

    2012-07-16T23:59:59.000Z

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

  5. A neural network approach to snack quality evaluation

    E-Print Network [OSTI]

    Sayeed, Mohammad Shaheen

    1994-01-01T23:59:59.000Z

    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...

  6. A portable neural network approach to vehicle tracking

    E-Print Network [OSTI]

    Miller, Kelly Maxwell

    1994-01-01T23:59:59.000Z

    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...

  7. Neural network based design of cellular manufacturing systems

    E-Print Network [OSTI]

    Ramachandran, Satheesh

    1990-01-01T23:59:59.000Z

    NEURAL NETWORK BASED DESIGN OF CELLULAR MANUFACTURING SYSTEMS A Thesis by SATHEESH RAMACHANDRAN Submitted to the Office of Graduate Studies of' Texas ASM University in partial fulfilltnent of the requirements for the degree of MASTER... OF SCIENCE December 1990 Major Subject: Industrial Engineering NEURAL NETWORK BASED DESIGN OF CELLULAR MANUFACTURING SYSTEMS A Thesis by SATHEESH RAMACHAiVDRAN Approved as to style and content by: Ce r O. Malav (Chair of Committee) T. Hsing...

  8. Experimental results of a predictive neural network HVAC controller

    SciTech Connect (OSTI)

    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

    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.

  9. Hybrid digital signal processing and neural networks applications in PWRs

    SciTech Connect (OSTI)

    Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

    1991-01-01T23:59:59.000Z

    Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications.

  10. Hybrid digital signal processing and neural networks applications in PWRs

    SciTech Connect (OSTI)

    Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

    1991-12-31T23:59:59.000Z

    Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications.

  11. A portable neural network approach to vehicle tracking

    E-Print Network [OSTI]

    Miller, Kelly Maxwell

    1994-01-01T23:59:59.000Z

    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...

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

    E-Print Network [OSTI]

    Spagnolo, Filippo

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

  13. A HARDWARE/SOFTWARE CO-DESIGN APPROACH FOR FACE RECOGNITION BY ARTIFICIAL NEURAL NETWORKS

    E-Print Network [OSTI]

    Areibi, Shawki M

    #12;A HARDWARE/SOFTWARE CO-DESIGN APPROACH FOR FACE RECOGNITION BY ARTIFICIAL NEURAL NETWORKS NETWORKS Xiaoguang Li University of Guelph, 2004 Advisor: Dr. Shawki M. Areibi Artificial Neural Networks Artificial Neural Networks on FPGAs. This work provided a very good beginning to my research. Special thanks

  14. Neural Network Based Intelligent Sootblowing System

    SciTech Connect (OSTI)

    Mark Rhode

    2005-04-01T23:59:59.000Z

    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.

  15. Biomimetic interfacial interpenetrating polymer networks control neural stem cell behavior

    E-Print Network [OSTI]

    Saha, Krishanu

    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

  16. A conjugate gradient learning algorithm for recurrent neural networks

    E-Print Network [OSTI]

    Mak, Man-Wai

    ]. 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

  17. APPLICATION OF THE FUZZY MIN-MAX NEURAL NETWORK CLASSIFIER

    E-Print Network [OSTI]

    Blekas, Konstantinos

    . 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

  18. APPLICATION OF THE FUZZY MINMAX NEURAL NETWORK CLASSIFIER

    E-Print Network [OSTI]

    Likas, Aristidis

    . 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

  19. APPLICATION OF THE FUZZY MINMAX NEURAL NETWORK CLASSIFIER

    E-Print Network [OSTI]

    Blekas, Konstantinos

    . 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

  20. Protein Sequence Classification Using Probabilistic Motifs and Neural Networks

    E-Print Network [OSTI]

    Blekas, Konstantinos

    Protein Sequence Classification Using Probabilistic Motifs and Neural Networks Konstantinos Blekas classification is the sequence encoding scheme that must be used in order to feed the network. To deal with this prob- lem we propose a method that maps a protein sequence into a numerical feature space using

  1. Neural Network Modeling of Degradation of Solar Cells

    SciTech Connect (OSTI)

    Gupta, Himanshu; Ghosh, Bahniman [Department of Electrical Engineering, Indian Institute of Technology, Kanpur, 208016 (India); Banerjee, Sanjay K. [Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, 78758 (United States)

    2011-05-25T23:59:59.000Z

    Neural network modeling has been used to predict the degradation in conversion efficiency of solar cells in this work. The model takes intensity of light, temperature and exposure time as inputs and predicts the conversion efficiency of the solar cell. Backpropagation algorithm has been used to train the network. It is found that the neural network model satisfactorily predicts the degradation in efficiency of the solar cell with exposure time. The error in the computed results, after comparison with experimental results, lies in the range of 0.005-0.01, which is quite low.

  2. Strategies for Spectral Profile Inversion using Artificial Neural Networks

    E-Print Network [OSTI]

    H. Socas-Navarro

    2004-10-23T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

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

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

    E-Print Network [OSTI]

    Toinet, Sylvain

    2001-01-01T23:59:59.000Z

    This study reports an investigation of the potentialities of artificial neural networks in the field of reservoir characterization. A first step has been the review of theoretical principles involved in neural networks computations, in order...

  5. Predicting Turbulence using Partial Least Squares Regression and an Artificial Neural Network

    E-Print Network [OSTI]

    Lakshmanan, Valliappa

    Predicting Turbulence using Partial Least Squares Regression and an Artificial Neural Network in the dataset. Then, the transformed data are pre- sented to a neural network whose output node has a sigmoid

  6. Neural Network Based Energy Storage System Modeling for Hybrid Electric Vehicles

    SciTech Connect (OSTI)

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

    1999-08-01T23:59:59.000Z

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

  7. Self-teaching neural network learns difficult reactor control problem

    SciTech Connect (OSTI)

    Jouse, W.C.

    1989-01-01T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    van Milligen, Boudewijn

    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

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

    E-Print Network [OSTI]

    Haviland, David

    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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    1 Optimization of an artificial neural network dedicated to the multivariate forecasting of daily Ajaccio, France Abstract. This paper presents an application of Artificial Neural Networks (ANNs Artificial Neural Networks (ANNs) which are a popular artificial intelligence technique in the forecasting

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

    E-Print Network [OSTI]

    Fernandez, Thomas

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

  12. Computational subunits of visual cortical neurons revealed by artificial neural networks

    E-Print Network [OSTI]

    Lau, Brian

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

  13. Application of Artificial Neural Network in Social Computing in the Context of Third World Countries

    E-Print Network [OSTI]

    Ghosh, Joydeep

    Application of Artificial Neural Network in Social Computing in the Context of Third World - In the last decade, applications associated with artificial neural network (ANN) has been gaining popularity amount of data, application of computational tools like Artificial Neural Network, Association rules

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

    E-Print Network [OSTI]

    Weeks, Eric R.

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

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

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

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

  17. A Predictive Model for Slip Resistance Using Artificial Neural Networks Janet M. Twomey, IIE Student Member

    E-Print Network [OSTI]

    Smith, Alice E.

    A Predictive Model for Slip Resistance Using Artificial Neural Networks Janet M. Twomey, IIE Artificial Neural Networks Why This Paper is Important Slips and falls are a serious ergonomic problem a slip resistance testing device were used to develop an artificial neural network model which predicts

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

    E-Print Network [OSTI]

    Vermont, University of

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

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

    E-Print Network [OSTI]

    Dawson, Michael

    Artificial neural network discrimination of black-capped chickadee (Poecile atricapillus) call; revised 11 April 2006; accepted 12 May 2006 Artificial neural networks were trained to discriminate number s : 43.80Ka., 43.80.Lb, 43.80.Jz JAS Pages: 11111117 I. INTRODUCTION An artificial neural network

  20. Automated Interpretation of Myocardial SPECT Perfusion Images Using Artificial Neural Networks

    E-Print Network [OSTI]

    Peterson, Carsten

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

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

    E-Print Network [OSTI]

    Vermont, University of

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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

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

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

    E-Print Network [OSTI]

    Wang, Yinhai

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

  4. Prediction of Protein Functional Domains from Sequences Using Artificial Neural Networks

    E-Print Network [OSTI]

    Szepesvari, Csaba

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

  5. Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks

    E-Print Network [OSTI]

    Perl, Jrgen

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

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

    E-Print Network [OSTI]

    Weeks, Eric R.

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

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

    E-Print Network [OSTI]

    Getz, Wayne M.

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

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

    E-Print Network [OSTI]

    Meeden, Lisa A.

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

  9. Batch-mode vs Online-mode Supervised Learning Motivations for Artificial Neural Networks

    E-Print Network [OSTI]

    Wehenkel, Louis

    Batch-mode vs Online-mode Supervised Learning Motivations for Artificial Neural Networks Linear ANN-mode vs Online-mode Supervised Learning Motivations for Artificial Neural Networks Linear ANN Models for Artificial Neural Networks Linear ANN Models Single neuron models Single layer models Nonlinear ANN Models

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

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

    Verleysen, Michel

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

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

    E-Print Network [OSTI]

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

  13. A Methodological Approach For Reservoir Heterogeneity Characterization Using Artificial Neural Networks

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    years Artificial Neural Networks (ANN) have made a strong comeback to the scientific community a handful of papers suggesting the use of artificial neural networks in the petroleum industry . These1 thought. An implementation of artificial neural networks in characterization of reservoir heterogeneity

  14. "Least Squares Fitting" Using Artificial Neural Networks YARON DANON and MARK J. EMBRECHTS

    E-Print Network [OSTI]

    Danon, Yaron

    "Least Squares Fitting" Using Artificial Neural Networks YARON DANON and MARK J. EMBRECHTS process changes the internal parameters (weights) of the network such that the neural net can represent a backpropagation fit to various continuous functions will be presented, showing properties of neural network fitted

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

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

  16. An artificial neural network controller for intelligent transportation systems applications

    SciTech Connect (OSTI)

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

    1996-04-01T23:59:59.000Z

    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.

  17. Real-time neural network earthquake profile predictor

    DOE Patents [OSTI]

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

    1996-01-01T23:59:59.000Z

    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.

  18. Real-time neural network earthquake profile predictor

    DOE Patents [OSTI]

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

    1996-02-06T23:59:59.000Z

    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.

  19. Nuclear power plant fault-diagnosis using artificial neural networks

    SciTech Connect (OSTI)

    Kim, Keehoon; Aljundi, T.L.; Bartlett, E.B.

    1992-01-01T23:59:59.000Z

    Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant's training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses.

  20. A neural network approach to burn-in

    E-Print Network [OSTI]

    Clifford, Nancy Lynn

    1995-01-01T23:59:59.000Z

    A NEURAL NETWORK APPROACH TO BURN-IN A Thesis by NANCY LYNN CLIFFORD 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 August 1995 Major... Subject: Industrial Enginccring A NEURAL NETWORK APPROACH TO BURN-IN A Thesis NANCY LYNN CLIFFORD Submitted to Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Approved as to style and content by...

  1. Use of neurals networks in nuclear power plant diagnostics

    SciTech Connect (OSTI)

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

    1989-01-01T23:59:59.000Z

    A technique using neural networks as a means of diagnosing transients or abnormal conditions in nuclear power plants is investigated and found to be feasible. The technique is based on the fact that each physical state of the plant can be represented by a unique pattern of sensor outputs or instrument readings that can be related to the condition of the plant. Neural networks are used to relate this pattern to the fault, problem, or transient condition of the plant. A demonstration of the ability of this technique to identify causes of perturbations in the steam generator of a nuclear plant is presented. 3 refs., 4 figs.

  2. Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks

    SciTech Connect (OSTI)

    Ziaul Huque

    2007-08-31T23:59:59.000Z

    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.

  3. Morphological Classification of Galaxies Using Artificial Neural Networks

    E-Print Network [OSTI]

    Nicholas M. Ball

    2001-10-22T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Dawson, Michael

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

  5. Neural network determination of parton distributions: the nonsinglet case

    E-Print Network [OSTI]

    The NNPDF Collaboration; Luigi Del Debbio; Stefano Forte; Jose I. Latorre; Andrea Piccione; Joan Rojo

    2007-01-16T23:59:59.000Z

    We provide a determination of the isotriplet quark distribution from available deep--inelastic data using neural networks. We give a general introduction to the neural network approach to parton distributions, which provides a solution to the problem of constructing a faithful and unbiased probability distribution of parton densities based on available experimental information. We discuss in detail the techniques which are necessary in order to construct a Monte Carlo representation of the data, to construct and evolve neural parton distributions, and to train them in such a way that the correct statistical features of the data are reproduced. We present the results of the application of this method to the determination of the nonsinglet quark distribution up to next--to--next--to--leading order, and compare them with those obtained using other approaches.

  6. Neural Networks ensemble for quality monitoring , M. Noyel1

    E-Print Network [OSTI]

    Boyer, Edmond

    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

  7. Adaptive Blind Signal Processing--Neural Network Approaches

    E-Print Network [OSTI]

    Vialatte, Franois

    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

  8. Nonlinear Flight Control Using Neural Networks and Feedback Linearization

    E-Print Network [OSTI]

    Nonlinear Flight Control Using Neural Networks and Feedback Linearization Byoung So0 Kim1, Anthony approaches to aircraft flight control involve linearization of these dynamicsabout a set of pre eliminate many of the undesirable features of linear control. Control of nonlinear systems by inverting

  9. Wind Power Plant Prediction by Using Neural Networks: Preprint

    SciTech Connect (OSTI)

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

    2012-08-01T23:59:59.000Z

    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.

  10. NEURAL NETWORK ASSISTED NONLINEAR CONTROLLER FOR A BIOREACTOR

    E-Print Network [OSTI]

    Efe, Mehmet nder

    343 NEURAL NETWORK ASSISTED NONLINEAR CONTROLLER FOR A BIOREACTOR Mehmet nder Efe Electrical Istanbul, 80815, Turkey ABSTRACT In this paper, design of a nonlinear controller for a Bioreactor Benchmark of such a simplified model is unlikely to result in a satisfactory performance. A prime example is a bioreactor

  11. 4 Neural Network Modelling 2 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

    E-Print Network [OSTI]

    Cambridge, University of

    of reality. Neural networks form a general method of non{linear regression. Their exibility enables them non{linear function is tted to experimental data, Fig 4.1 as in linear regression, the input variable are derived. The general form of the equation developed using linear regression is a sum of the products

  12. Neural network model of creep strength of austenitic stainless steels

    E-Print Network [OSTI]

    Cambridge, University of

    is a parameterised non-linear model which can be used to perform regression, in which case, a very exible, non-linear of the problems encoun- tered with linear regression. In the present study, neural network analysis was applied with a constant input set to unity. Any non-linear function can be used at the hidden units (as long

  13. RESERVOIR INFLOW FORECASTING USING NEURAL NETWORKS CHANDRASHEKAR SUBRAMANIAN

    E-Print Network [OSTI]

    Manry, Michael

    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 technology for inflow forecasting, because of (1) their success in power load forecasting 1- 6 , and (2

  14. Neural Networks for Post-processing Model Output: Caren Marzban

    E-Print Network [OSTI]

    Marzban, Caren

    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

  15. Practical Variational Inference for Neural Networks Alex Graves

    E-Print Network [OSTI]

    Toronto, University of

    are energy-based models such as restricted Boltzmann machines [24] whose log- loss is intractable. 1 #12-loss parametric model--which includes most neural networks1 Variational inference can be reformulated as the optimisation of a Minimum Description length (MDL; [21]) loss function; indeed it was in this form

  16. Optimisation of Neural Network for Charpy Toughness of Steel Welds

    E-Print Network [OSTI]

    Cambridge, University of

    Optimisation of Neural Network for Charpy Toughness of Steel Welds Jun Hak, Pak #12;Contents Introduction Modelling of output Bias in Models Interpass temperature effect Results Discussion Conclusions #12) Measured energy absorbed by a standard s ample during fracture Never be negative ! #12;Example of non

  17. Optimisation of Neural Network for Charpy Toughness of Steel Welds

    E-Print Network [OSTI]

    Cambridge, University of

    Optimisation of Neural Network for Charpy Toughness of Steel Welds Jun Hak, Pak #12;Contents Introduction Modelling of output Bias in Models Interpass temperature effectInterpass temperature effect unphysical values PossiblePossible Charpy toughness (Impact toughness) Measured energy absorbed by a standard

  18. Successful neural network projects at the Idaho National Engineering Laboratory

    SciTech Connect (OSTI)

    Cordes, G.A.

    1991-01-01T23:59:59.000Z

    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.

  19. Neural Network Control of a Pneumatic Robot Ted Hesselroth

    E-Print Network [OSTI]

    Duckett, Tom

    Neural Network Control of a Pneumatic Robot Arm Ted Hesselroth , Kakali Sarkar , P. Patrick van der been employed to control a five-joint pneu- matic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm (SoftArm) employed in this inves- tigation shares essential

  20. Transmission Line Boundary Protection Using Wavelet Transform and Neural Network

    E-Print Network [OSTI]

    use data from two ends. The non-unit protection such as distance relay, can not protect the entire end only. In this case, the relay at one end can protect the entire line length with no intentional1 Transmission Line Boundary Protection Using Wavelet Transform and Neural Network Nan Zhang

  1. Hybrid Neural Network for Gas Analysis Measuring System Kazimierz Brudzewski

    E-Print Network [OSTI]

    Osowski, Stanislaw

    mixtures of air with these four pollutants. The signals obtained from the sensors have been processed using with hybrid neural network, can be used to determine the individual analyte concentrations in the mixture as pollutants: carbon oxide, methane, propane/buthane and methanol vapour. A small array of five semiconductor

  2. Forecasting Hospital Bed Availability Using Simulation and Neural Networks

    E-Print Network [OSTI]

    Kuhl, Michael E.

    Forecasting Hospital Bed Availability Using Simulation and Neural Networks Matthew J. Daniels, NY 14623 Elisabeth Hager Hager Consulting Pittsford, NY 14534 Abstract The availability of beds is a critical factor for decision-making in hospitals. Bed availability (or alternatively the bed occupancy

  3. Characterization of Shape Memory Alloys Using Artificial Neural Networks

    E-Print Network [OSTI]

    Valasek, John

    Characterization of Shape Memory Alloys Using Artificial Neural Networks James V. Henrickson , Kenton Kirkpatrick, and John Valasek Texas A&M University, College Station, Texas 77843-3141 Shape memory of shape memory alloys, however, is often com- plicated by their hysteretic, non-linear, thermo

  4. Vibration monitoring of EDF rotating machinery using artificial neural networks

    SciTech Connect (OSTI)

    Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E. (Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering); Hamon, L.; Lefevre, F. (Electricite de France, 78 - Chatou (France). Direction des Etudes et Recherches)

    1991-01-01T23:59:59.000Z

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging.

  5. Vibration monitoring of EDF rotating machinery using artificial neural networks

    SciTech Connect (OSTI)

    Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering; Hamon, L.; Lefevre, F. [Electricite de France, 78 - Chatou (France). Direction des Etudes et Recherches

    1991-12-31T23:59:59.000Z

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging.

  6. Proceedings of the Neural Network Workshop for the Hanford Community

    SciTech Connect (OSTI)

    Keller, P.E.

    1994-01-01T23:59:59.000Z

    These proceedings were generated from a series of presentations made at the Neural Network Workshop for the Hanford Community. The abstracts and viewgraphs of each presentation are reproduced in these proceedings. This workshop was sponsored by the Computing and Information Sciences Department in the Molecular Science Research Center (MSRC) at the Pacific Northwest Laboratory (PNL). Artificial neural networks constitute a new information processing technology that is destined within the next few years, to provide the world with a vast array of new products. A major reason for this is that artificial neural networks are able to provide solutions to a wide variety of complex problems in a much simpler fashion than is possible using existing techniques. In recognition of these capabilities, many scientists and engineers are exploring the potential application of this new technology to their fields of study. An artificial neural network (ANN) can be a software simulation, an electronic circuit, optical system, or even an electro-chemical system designed to emulate some of the brain`s rudimentary structure as well as some of the learning processes that are believed to take place in the brain. For a very wide range of applications in science, engineering, and information technology, ANNs offer a complementary and potentially superior approach to that provided by conventional computing and conventional artificial intelligence. This is because, unlike conventional computers, which have to be programmed, ANNs essentially learn from experience and can be trained in a straightforward fashion to carry out tasks ranging from the simple to the highly complex.

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

    E-Print Network [OSTI]

    Vermont, University of

    July 2007; published 8 November 2007. [1] A novel data-driven artificial neural network (ANN and spatial estimation with multiple data types using artificial neural networks, Water Resour. Res., 43, WStochastic simulation and spatial estimation with multiple data types using artificial neural

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

    E-Print Network [OSTI]

    Oldenburg, Carl von Ossietzky Universitt

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

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

    E-Print Network [OSTI]

    Lek, Sovan

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

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

    E-Print Network [OSTI]

    Chaubey, Indrajeet

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

  11. Predicting stream water quality using artificial neural networks (ANN)

    SciTech Connect (OSTI)

    Bowers, J.A.

    2000-05-17T23:59:59.000Z

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

  12. Artificial Neural Networks for Solving Ordinary and Partial Differential Equations

    E-Print Network [OSTI]

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

    1997-05-19T23:59:59.000Z

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

  13. A stochastic learning algorithm for layered neural networks

    SciTech Connect (OSTI)

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

    1992-01-01T23:59:59.000Z

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

  14. A stochastic learning algorithm for layered neural networks

    SciTech Connect (OSTI)

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

    1992-12-31T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Liblit, Ben

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

  16. Feasibility of using neural networks as a level 2 calorimeter trigger for jet tagging

    SciTech Connect (OSTI)

    Handler, T.; Neis, E. [Tennessee Univ., Knoxville, TN (United States). Dept. of Physics; Glover, C.; Gabriel, T.; Saini, S. [Oak Ridge National Lab., TN (United States)

    1992-12-31T23:59:59.000Z

    Several of expected decay modes of the Higgs particle will result in jet formation. We propose to incorporate a second level trigger into the SDC detector, using neural network VSLI hardware, to tag such Higgs decay modes. The input to the neural network will be the energy depositions in both the barrel and endcap regions of the calorimeter. The neural network`s output would be a value representing the degree of correlation between the observed energy distribution and the type of physical scattering that has occurred. Preliminary results indicate that neural networks may be of use in tagging jet decays of the Higgs particle.

  17. A DISCRETE APPROACH TO CONSTRUCTIVE NEURAL NETWORK

    E-Print Network [OSTI]

    Obradovic, Zoran

    and the Paragon supercomputer using p4 are in agreement with analytical speed-up estimates and the architecture is a weighted graph of simple processing units (or neurons). The interconnection graph of a feed-forward network)) involves modi cation of the interconnection weights between neurons on a pre-speci ed network. Determining

  18. Nodes self-deployment for coverage maximization in mobile robot networks using an evolving neural network

    E-Print Network [OSTI]

    Boyer, Edmond

    such as energy consumption, throughput, delay, coverage, etc. Also many schemes have been proposed in order. In fact, it is sufficient to consider a different input for the neural network to aim to a different monitoring and smart agriculture [1]. One of the fundamental issue in such a network is coverage. It is used

  19. Random Weights Search in CompressedNeural Networks Using OverdeterminedPseudoinverse

    E-Print Network [OSTI]

    Wilamowski, Bogdan Maciej

    regions by variable tabu list size. Overvieu, of combination of neural network gradient search and TabuRandom Weights Search in CompressedNeural Networks Using OverdeterminedPseudoinverse Milos Manic is reduction of weight set. Second phase is gradient calculation on such compressed network. Search for weights

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    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

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

    E-Print Network [OSTI]

    Ryan, Michael J.

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

  2. Adaptive model predictive process control using neural networks

    DOE Patents [OSTI]

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

    1997-01-01T23:59:59.000Z

    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.

  3. Adaptive model predictive process control using neural networks

    DOE Patents [OSTI]

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

    1997-08-19T23:59:59.000Z

    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.

  4. Laser programmable integrated curcuit for forming synapses in neural networks

    DOE Patents [OSTI]

    Fu, Chi Y. (San Francisco, CA)

    1997-01-01T23:59:59.000Z

    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.

  5. Process for forming synapses in neural networks and resistor therefor

    DOE Patents [OSTI]

    Fu, Chi Y. (San Francisco, CA)

    1996-01-01T23:59:59.000Z

    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.

  6. Feasibility of using neural networks as a level 2 calorimeter trigger for jet tagging

    SciTech Connect (OSTI)

    Handler, T.; Neis, E. (Tennessee Univ., Knoxville, TN (United States). Dept. of Physics); Glover, C.; Gabriel, T.; Saini, S. (Oak Ridge National Lab., TN (United States))

    1992-01-01T23:59:59.000Z

    Several of expected decay modes of the Higgs particle will result in jet formation. We propose to incorporate a second level trigger into the SDC detector, using neural network VSLI hardware, to tag such Higgs decay modes. The input to the neural network will be the energy depositions in both the barrel and endcap regions of the calorimeter. The neural network's output would be a value representing the degree of correlation between the observed energy distribution and the type of physical scattering that has occurred. Preliminary results indicate that neural networks may be of use in tagging jet decays of the Higgs particle.

  7. 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...

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

    E-Print Network [OSTI]

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

    2010-01-01T23:59:59.000Z

    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...

  9. Nuclear power plant fault-diagnosis using artificial neural networks

    SciTech Connect (OSTI)

    Kim, Keehoon; Aljundi, T.L.; Bartlett, E.B.

    1992-12-31T23:59:59.000Z

    Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant`s training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses.

  10. Artificial Neural Networks and quadratic Response Surfaces for the functional failure analysis of a thermal-hydraulic passive system

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    system reliability, artificial neural network, quadratic response surface 1. INTRODUCTION Modern nuclearArtificial Neural Networks and quadratic Response Surfaces for the functional failure analysis of a thermal-hydraulic passive system George Apostolakisa , Nicola Pedronib , Enrico Ziob* a Massachusetts

  11. Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks

    SciTech Connect (OSTI)

    Nelson Butuk

    2006-09-21T23:59:59.000Z

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

  12. 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

  13. NEURAL NETWORK BASED CLASSIFIER FOR ULTRASONIC RESONANCE SPECTRA Tadeusz Stepinski, Lars Ericsson, Bengt Vagnhammar and Mats Gustafsson

    E-Print Network [OSTI]

    , Sweden (To appear in Proceedings of the 7th ECNDT In this paper we present a neural network

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

    E-Print Network [OSTI]

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

    2004-01-01T23:59:59.000Z

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

  15. Reconstruction of Flaw Profiles Using Neural Networks and Multi-Frequency Eddy Current System

    SciTech Connect (OSTI)

    Chady, T.; Caryk, M. [Technical University of Szczecin, al. Piastow 19, 70-310 Szczecin (Poland)

    2005-04-09T23:59:59.000Z

    The objective of this paper is to identify profiles of flaws in conducting plates. To solve this problem, application of a multi-frequency eddy current system (MFES) and artificial neural networks is proposed. Dynamic feed-forward neural networks with various architectures are investigated. Extended experiments with all neural models are carried out in order to select the most promising configuration. Data utilized for the experiments were obtained from the measurements performed on the Inconel plates with EDM flaws.

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

    E-Print Network [OSTI]

    Ahmed, Farid

    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

  17. GAS ANALYSIS SYSTEM COMPOSED OF A SOLID-STATE SENSOR ARRAY AND HYBRID NEURAL NETWORK

    E-Print Network [OSTI]

    Osowski, Stanislaw

    1 GAS ANALYSIS SYSTEM COMPOSED OF A SOLID-STATE SENSOR ARRAY AND HYBRID NEURAL NETWORK STRUCTURE of the solid state sensor array used for the gas analysis. The applied neural network is composed of two parts of the gas components. The obtained results have shown that the array of partially selective sensors

  18. Alternatives to Energy FunctionBased Analysis of Recurrent Neural Networks

    E-Print Network [OSTI]

    Hassoun, Mohamad H.

    1 Alternatives to Energy FunctionBased Analysis of Recurrent Neural Networks Mohamad H. Hassoun of analysis and design of recurrent neural networks using an energy function approach, as popularized, 1990]. Such type of analysis, though, usually imposes stringent and/or biologically unrealistic

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

    E-Print Network [OSTI]

    Kaber, David B.

    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

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

    E-Print Network [OSTI]

    Potter, Don

    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

  1. Real-time Control of a Tokamak Plasma Using Neural Networks

    E-Print Network [OSTI]

    Bishop, Christopher M.

    Real-time Control of a Tokamak Plasma Using Neural Networks Christopher M. Bishop , Paul S. Haynes of neural networks for real-time control of the high temperature plasma in a tokamak fusion experiment. The tokamak is currently the principal experimental device for research into the magnetic confinement approach

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    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

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

    E-Print Network [OSTI]

    Cambridge, University of

    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

  4. Sliding Mode Adaptive Neural-network Control for Nonholonomic Mobile Modular Manipulators

    E-Print Network [OSTI]

    Li, Yangmin

    Sliding Mode Adaptive Neural-network Control for Nonholonomic Mobile Modular Manipulators Yangmin adaptive neural-network controller for trajectory following of nonholonomic mobile modular manipulators model of the mobile modular manipulator. Sliding mode control and direct adaptive technique are combined

  5. Fingerprinting Localization based on Neural Networks and Ultra-wideband signals

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    by processing an IR-UWB indoor propagation measurement campaign. The construction of the neural networks and different sizes of the fingerprinting database. Index Terms--Fingerprinting, Localization, Neural networks]. A good indoor localization system must have a high accuracy, a short training phase, a low cost

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

    E-Print Network [OSTI]

    Molter, Colin

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

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

    E-Print Network [OSTI]

    Lerner, Boaz

    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

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

    E-Print Network [OSTI]

    Yao, Xin

    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

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

    E-Print Network [OSTI]

    Cambridge, University of

    , 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

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

    E-Print Network [OSTI]

    Boyer, Edmond

    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

  11. Process Planning Using An Integrated Expert System And Neural Network Approach

    E-Print Network [OSTI]

    Smith, Alice E.

    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

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

    E-Print Network [OSTI]

    Peterson, Carsten

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

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

    E-Print Network [OSTI]

    Dawson, Michael

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

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

    E-Print Network [OSTI]

    Bax, Ad

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

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

    E-Print Network [OSTI]

    Sontag, Eduardo

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

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

    E-Print Network [OSTI]

    Nielsen, Finn rup

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

  17. Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks

    E-Print Network [OSTI]

    Zhuang, Qianlai

    artificial neural networks Xudong Zhu,1 Qianlai Zhuang,1 Zhangcai Qin,1 Mikhail Glagolev,2 and Lulu Song1 develop a statistical model of CH4 emissions using an artificial neural network (ANN) approach and field to 2009 using artificial neural networks, Global Biogeochem. Cycles, 27, doi:10.1002/gbc.20052. 1

  18. Neural Network Based Montioring and Control of Fluidized Bed.

    SciTech Connect (OSTI)

    Bodruzzaman, M.; Essawy, M.A.

    1996-04-01T23:59:59.000Z

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

  19. Tutorial: Neural networks and their potential application in nuclear power plants

    SciTech Connect (OSTI)

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

    1989-01-01T23:59:59.000Z

    A neural network is a data processing system consisting of a number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Examples include reading Japanese Kanji characters and human handwriting, reading a typewritten manuscript aloud, compensating for alignment errors in robots, interpreting very noise'' signals (e.g. electroencephalograms), modeling complex systems that cannot be modelled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and describes research on the potential applications to nuclear power plants.

  20. Structural Health Monitoring Using Neural Network Based Vibrational System Identification

    E-Print Network [OSTI]

    Sofge, Donald A

    2007-01-01T23:59:59.000Z

    Composite fabrication technologies now provide the means for producing high-strength, low-weight panels, plates, spars and other structural components which use embedded fiber optic sensors and piezoelectric transducers. These materials, often referred to as smart structures, make it possible to sense internal characteristics, such as delaminations or structural degradation. In this effort we use neural network based techniques for modeling and analyzing dynamic structural information for recognizing structural defects. This yields an adaptable system which gives a measure of structural integrity for composite structures.

  1. An artificial neural network application on nuclear charge radii

    E-Print Network [OSTI]

    Akkoyun, S; Kara, S O; Sinan, A

    2013-01-01T23:59:59.000Z

    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.

  2. Artificial Neural Networks as a Tool for Galaxy Classification

    E-Print Network [OSTI]

    Ofer Lahav

    1996-12-10T23:59:59.000Z

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

  3. An artificial neural network application on nuclear charge radii

    E-Print Network [OSTI]

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

    2012-12-27T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Smith, Alice E.

    Estimation of All-Terminal Network Reliability Using an Artificial Neural Network Chat Srivaree be forwarded) Submitted to Computers & Operations Research June 1999 #12;1 Estimation of All-Terminal Network or communications network topologies, a common reliability measure is all-terminal reliability, the probability

  5. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Practical Guidelines for Developing BP Neural Network Models of Measurement U...

    E-Print Network [OSTI]

    Smith, Alice E.

    permission. Practical Guidelines for Developing BP Neural Network Models of Measurement U... Chang-Xue Jack

  6. On the deduction of galaxy abundances with evolutionary neural networks

    E-Print Network [OSTI]

    Michael Taylor; Angeles I. Diaz

    2007-09-19T23:59:59.000Z

    A growing number of indicators are now being used with some confidence to measure the metallicity(Z) of photoionisation regions in planetary nebulae, galactic HII regions(GHIIRs), extra-galactic HII regions(EGHIIRs) and HII galaxies(HIIGs). However, a universal indicator valid also at high metallicities has yet to be found. Here, we report on a new artificial intelligence-based approach to determine metallicity indicators that shows promise for the provision of improved empirical fits. The method hinges on the application of an evolutionary neural network to observational emission line data. The network's DNA, encoded in its architecture, weights and neuron transfer functions, is evolved using a genetic algorithm. Furthermore, selection, operating on a set of 10 distinct neuron transfer functions, means that the empirical relation encoded in the network solution architecture is in functional rather than numerical form. Thus the network solutions provide an equation for the metallicity in terms of line ratios without a priori assumptions. Tapping into the mathematical power offered by this approach, we applied the network to detailed observations of both nebula and auroral emission lines in the optical for a sample of 96 HII-type regions and we were able to obtain an empirical relation between Z and S23 with a dispersion of only 0.16 dex. We show how the method can be used to identify new diagnostics as well as the nonlinear relationship supposed to exist between the metallicity Z, ionisation parameter U and effective (or equivalent) temperature T*.

  7. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    SciTech Connect (OSTI)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E. [Tennessee Univ., Knoxville, TN (United States); Mullens, J.A. [Tennessee Univ., Knoxville, TN (United States)]|[Oak Ridge National Lab., TN (United States)

    1991-12-01T23:59:59.000Z

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN`s provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ``virtual measurements`` the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab.

  8. Self-organizing neural network as a fuzzy classifier

    SciTech Connect (OSTI)

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

    1994-03-01T23:59:59.000Z

    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.

  9. An evaluation of neural networks for identification of system parameters in reactor noise signals

    SciTech Connect (OSTI)

    Miller, L.F.

    1991-12-31T23:59:59.000Z

    Several backpropagation neural networks for identifying fundamental mode eigenvalues were evaluated. The networks were trained and tested on analytical data and on results from other numerical methods. They were then used to predict first mode break frequencies for noise data from several sources. These predictions were, in turn, compared with analytical values and with results from alternative methods. Comparisons of results for some data sets suggest that the accuracy of predictions from neural networks are essentially equivalent to results from conventional methods while other evaluations indicate that either method may be superior. Experience gained from these numerical experiments provide insight for improving the performance of neural networks relative to other methods for identifying parameters associated with experimental data. Neural networks may also be used in support of conventional algorithms by providing starting points for nonlinear minimization algorithms.

  10. An evaluation of neural networks for identification of system parameters in reactor noise signals

    SciTech Connect (OSTI)

    Miller, L.F.

    1991-01-01T23:59:59.000Z

    Several backpropagation neural networks for identifying fundamental mode eigenvalues were evaluated. The networks were trained and tested on analytical data and on results from other numerical methods. They were then used to predict first mode break frequencies for noise data from several sources. These predictions were, in turn, compared with analytical values and with results from alternative methods. Comparisons of results for some data sets suggest that the accuracy of predictions from neural networks are essentially equivalent to results from conventional methods while other evaluations indicate that either method may be superior. Experience gained from these numerical experiments provide insight for improving the performance of neural networks relative to other methods for identifying parameters associated with experimental data. Neural networks may also be used in support of conventional algorithms by providing starting points for nonlinear minimization algorithms.

  11. Applications of Artificial Neural Networks and Fuzzy Models in High Throughput Screening: Classifying the activities of various compounds towards Cobalt

    E-Print Network [OSTI]

    Kansas, University of

    Applications of Artificial Neural Networks and Fuzzy Models in High Throughput Screening to the existing HTS method, via Quantitative Structure-Activity Relationship (QSAR) using Artificial Neural in solving non-linear pattern classification problems, we propose several different models of neural networks

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

    E-Print Network [OSTI]

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

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

    SciTech Connect (OSTI)

    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

    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.

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

    E-Print Network [OSTI]

    1999-01-01T23:59:59.000Z

    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.

  15. This is the documentation for the yield strength neural network, as described in Program MAP_NEURAL_AUSTENITIC_YIELD

    E-Print Network [OSTI]

    Cambridge, University of

    temperatures, as a function of chemical composition and heat treatments. Specification Language: FORTRAN / C The modelling procedure is a purely empirical one, and is based on a neural network program called generate44, which was developed by David MacKay and is part of the bigback5 program. The model is constituted

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

    E-Print Network [OSTI]

    Choe, Yoonsuck

    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

  17. Artificial Neural Network for search for metal poor galaxies

    E-Print Network [OSTI]

    Shi, F; Kong, X; Chen, Y

    2013-01-01T23:59:59.000Z

    In order to find a fast and reliable method for selecting metal poor galaxies (MPGs), especially in large surveys and huge database, an Artificial Neural Network (ANN) method is applied to a sample of star-forming galaxies from the Sloan Digital Sky Survey (SDSS) data release 9 (DR9) provided by the Max Planck Institute and the Johns Hopkins University (MPA/JHU). A two-step approach is adopted:(i) The ANN network must be trained with a subset of objects that are known to be either MPGs or MRGs(Metal Rich galaxies), treating the strong emission line flux measurements as input feature vectors in an n-dimensional space, where n is the number of strong emission line flux ratios. (ii) After the network is trained on a sample of star-forming galaxies, remaining galaxies are classified in the automatic test analysis as either MPGs or MRGs. We consider several random divisions of the data into training and testing sets: for instance, for our sample, a total of 70 percent of the data are involved in training the algor...

  18. Drive reinforcement neural networks for reactor control. Final report

    SciTech Connect (OSTI)

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

    1995-02-01T23:59:59.000Z

    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.

  19. APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION

    SciTech Connect (OSTI)

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

    2012-11-01T23:59:59.000Z

    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.

  20. Regular graphs maximize the variability of random neural networks

    E-Print Network [OSTI]

    Gilles Wainrib; Mathieu Galtier

    2014-12-17T23:59:59.000Z

    In this work we study the dynamics of systems composed of numerous interacting elements interconnected through a random weighted directed graph, such as models of random neural networks. We develop an original theoretical approach based on a combination of a classical mean-field theory originally developed in the context of dynamical spin-glass models, and the heterogeneous mean-field theory developed to study epidemic propagation on graphs. Our main result is that, surprisingly, increasing the variance of the in-degree distribution does not result in a more variable dynamical behavior, but on the contrary that the most variable behaviors are obtained in the regular graph setting. We further study how the dynamical complexity of the attractors is influenced by the statistical properties of the in-degree distribution.

  1. Galaxy Classification by Human Eyes and by Artificial Neural Networks

    E-Print Network [OSTI]

    Ofer Lahav

    1995-05-19T23:59:59.000Z

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

  2. ANNz: estimating photometric redshifts using artificial neural networks

    E-Print Network [OSTI]

    Adrian A. Collister; Ofer Lahav

    2004-02-27T23:59:59.000Z

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

  3. Nuclear power plant status diagnostics using artificial neural networks

    SciTech Connect (OSTI)

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

    1991-12-31T23:59:59.000Z

    In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results.

  4. Nuclear power plant status diagnostics using artificial neural networks

    SciTech Connect (OSTI)

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

    1991-01-01T23:59:59.000Z

    In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results.

  5. Active control of SDF systems using artificial neural networks.

    SciTech Connect (OSTI)

    Tang, Y.; Reactor Engineering

    1997-01-01T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Evans, Brian L.

    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

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

    E-Print Network [OSTI]

    Yamaha, M.; Takahashi, M.

    2004-01-01T23:59:59.000Z

    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...

  8. Satisfiability of logic programming based on radial basis function neural networks

    SciTech Connect (OSTI)

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

    2014-07-10T23:59:59.000Z

    In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.

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

    E-Print Network [OSTI]

    Oldenburg, Carl von Ossietzky Universitt

    methods to examine crude oils, heavy refined oils, and sludge oils: the channels relationships method (CRMClassification with Artificial Neural Networks and Support Vector Machines: application to oil, and Oil fluorescence ABSTRACT: This paper reports on oil classification with fluorescence spectroscopy

  10. Fractional Snow-Cover Mapping Through Artificial Neural Network Analysis of MODIS Surface Reflectance.

    E-Print Network [OSTI]

    Dobreva, Iliyana D.

    2010-07-14T23:59:59.000Z

    unmixing and the empirical Normalized Difference Snow Index (NDSI) method. Machine learning is an alternative to these approaches for estimating FSC, as Artificial Neural Networks (ANNs) have been used for estimating the subpixel abundances of other...

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

    E-Print Network [OSTI]

    Agarwal, Shankar

    2012-12-31T23:59:59.000Z

    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 ...

  12. 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

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

    E-Print Network [OSTI]

    Long, Xiao

    2012-10-19T23:59:59.000Z

    performance of moment frames. Without such a model, the aforementioned problem can never be resolved. This dissertation develops an innovative way of predicting cyclic rupture in steel moment frames by employing artificial neural networks. First, finite...

  14. Development of neural network calibration algorithms for multi-port pressure probes

    E-Print Network [OSTI]

    Kinser Robert Eric

    1996-01-01T23:59:59.000Z

    The development of an enhanced neural network training algorithm and program for the calibration of velocity measurement instrumentation is presented. The backpropagation-based program, PROBENET, is intended to be a robust self learning code...

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

    E-Print Network [OSTI]

    Kao, Ling-Jing

    2001-01-01T23:59:59.000Z

    . 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...

  16. Data driven process monitoring based on neural networks and classification trees

    E-Print Network [OSTI]

    Zhou, Yifeng

    2005-11-01T23:59:59.000Z

    of this dissertation is to develop process monitoring schemes that can be applied to complex process systems. Neural networks have been a popular tool for modeling and pattern classification for monitoring of process systems. However, due to the prohibitive...

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

    E-Print Network [OSTI]

    Voudris, Athanasios V

    2006-01-01T23:59:59.000Z

    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 ...

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

    E-Print Network [OSTI]

    Johnson, M. L.

    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...

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

    E-Print Network [OSTI]

    Yamaha, M.; Takahashi, M.

    2004-01-01T23:59:59.000Z

    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...

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

    E-Print Network [OSTI]

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

    2005-11-30T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Chang, Joongho

    1994-01-01T23:59:59.000Z

    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...

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

    E-Print Network [OSTI]

    Schnorrenberg, Frank Theo

    1992-01-01T23:59:59.000Z

    NEURAL NETWORK TECHNOLOGY FOR AUTOMATIC FRACTURE DETECTION IN SONIC BOREHOLE IMAGE DATA A Thesis by FRANK THEO SCHNORRENBERG Submitted to the Office of Graduate Studies of Texas A&M University tn partial fulfillment of the requirements... for the degree of MASTER OF SC1ENCE December 1992 Major Subject: Computer Science NEURAL NETWORK TECHNOLOGY FOR AUTOMATIC FRACTURE DETECTION IN SONIC BOREHOLE IMAGE DATA A Thesis by FRANK THEO SCHNORRENBERG Subnutted to the Office of Graduate Studies...

  3. Classification of brain compartments and head injury lesions by neural networks applied to magnetic resonance images

    E-Print Network [OSTI]

    Kischell, Eric Robert

    1993-01-01T23:59:59.000Z

    ' (Member) A. D. Patton ( ead of epartment) August 1993 Major Subject: Electrical Engineering ABSTRACT Classification of Brain Compartments and Head Injury Lesions by Neural Networks Applied to Magnetic Resonance Images. (August 1993) Eric Robert... Kischell, B. S. , Northeastern University Chair of Advisory Committee: Dr. Nasser Kehtarnavaz An automatic neural network-based approach was ap- plied to segment brain compartments and closed-head-injury lesions in magnetic resonance (MR) images Two...

  4. Incipient fault detection and identification in process systems using artificial neural networks

    E-Print Network [OSTI]

    Muthusami, Jayakumar

    1992-01-01T23:59:59.000Z

    INCIPIENT FAULT DETECTION AND IDENTIFICATION IN PROCESS SYSTEMS USING ARTIFICIAL NEURAL NETWORKS A Thesis by JAYAKUMAR MUTHUSAMI 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 December 1992 Major Subject: Mechanical Engineering INCIPIENT FAULT DETECTION AND IDENTIFICATION IN PROCESS SYSTEMS USING ARTIFICIAL NEURAL NETWORKS A Thesis JAYAKHMAH MUTHTJSAMI Approved a. s to style and content, by...

  5. Use of neural networks to identify transient operating conditions in nuclear power plants

    SciTech Connect (OSTI)

    Uhrig, R.E.; Guo, Z.

    1989-01-01T23:59:59.000Z

    A technique using neural networks as a means of diagnosing specific abnormal conditions or problems in nuclear power plants is investigated and found to be feasible. The technique is based on the fact that each physical state of the plant can be represented by a unique pattern of instrument readings, which can be related to the condition of the plant. Neural networks are used to relate this pattern to the fault or problem. 3 refs., 2 figs., 4 tabs.

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

    E-Print Network [OSTI]

    Sontag, Eduardo

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

  7. NEURAL NETWORK BASED CLASSIFIER FOR ULTRASONIC RESONANCE SPECTRA Tadeusz Stepinski, Lars Ericsson, Bengt Vagnhammar and Mats Gustafsson

    E-Print Network [OSTI]

    , Sweden (To appear in Proceedings of the 7 th ECNDT 6i+#...hp# In this paper we present a neural network

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

    SciTech Connect (OSTI)

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

    1996-12-31T23:59:59.000Z

    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.

  9. Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil

    E-Print Network [OSTI]

    Kretchmar, R. Matthew

    situations, the degree of uncertainty in the model of the system being controlled limits the utility networks. Section 3 describes how a simple inverse model can be used to train a neural-network controller performance. 2 Heating Coil Model and PI Control Underwood and Crawford [9] developed a model of an existing

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

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    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

  11. Neurocomputing 70 (2006) 603606 Stability analysis of an unsupervised neural network

    E-Print Network [OSTI]

    Pilyugin, Sergei S.

    2006-01-01T23:59:59.000Z

    and neurons forming complicated dynamics. 2. Equilibrium and global asymptotic stability analysis of neuro-synapticNeurocomputing 70 (2006) 603606 Letters Stability analysis of an unsupervised neural network neuron of a N-neuron network are STM: _xj ajxj Cj XN i1 Dijf xi Bj XN i1 mijyi, (1) LTM 1

  12. Application of an artificial neural network to reactor core analysis

    SciTech Connect (OSTI)

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

    1995-12-31T23:59:59.000Z

    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.

  13. Multi-parameter estimating photometric redshifts with artificial neural networks

    E-Print Network [OSTI]

    Lili Li; Yanxia Zhang; Yongheng Zhao; Dawei Yang

    2007-04-17T23:59:59.000Z

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

  14. Automatic Detection of Expanding HI Shells Using Artificial Neural Networks

    E-Print Network [OSTI]

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

    2003-04-17T23:59:59.000Z

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

  15. Neural network recognition of nuclear power plant transients

    SciTech Connect (OSTI)

    Bartlett, E.B.; Danofsky, R.; Adams, J.; AlJundi, T.; Basu, A.; Dhanwada, C.; Kerr, J.; Kim, K.; Lanc, T.

    1993-02-23T23:59:59.000Z

    The objective of this report is to describe results obtained during the first year of funding that will lead to the development of an artificial neural network (ANN) fault - diagnostic system for the real - time classification of operational transients at nuclear power plants. The ultimate goal of this three-year project is to design, build, and test a prototype diagnostic adviser for use in the control room or technical support center at Duane Arnold Energy Center (DAEC); such a prototype could be integrated into the plant process computer or safety - parameter display system. The adviser could then warn and inform plant operators and engineers of plant component failures in a timely manner. This report describes the work accomplished in the first of three scheduled years for the project. Included herein is a summary of the first year's results as, well as individual descriptions of each of the major topics undertaken by the researchers. Also included are reprints of the articles written under this funding as well as those that were published during the funded period.

  16. Using artificial neural network tools to analyze microbial biomarker data

    SciTech Connect (OSTI)

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

    2004-03-17T23:59:59.000Z

    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.

  17. Evidence for single top quark production using Bayesian neural networks

    SciTech Connect (OSTI)

    Kau, Daekwang; /Florida State U.

    2007-08-01T23:59:59.000Z

    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.

  18. Modeling of transport phenomena in tokamak plasmas with neural networks

    SciTech Connect (OSTI)

    Meneghini, O., E-mail: meneghini@fusion.gat.com [Oak Ridge Associated Universities, 120 Badger Ave, Oak Ridge, Tennessee 37830 (United States); Luna, C. J. [Arizona State University, 411 N. Central Ave, Phoenix, Arizona 85004 (United States); Smith, S. P.; Lao, L. L. [General Atomics, San Diego, California 92186-5608 (United States)

    2014-06-15T23:59:59.000Z

    A new transport model that uses neural networks (NNs) to yield electron and ion heat flux profiles has been developed. Given a set of local dimensionless plasma parameters similar to the ones that the highest fidelity models use, the NN model is able to efficiently and accurately predict the ion and electron heat transport profiles. As a benchmark, a NN was built, trained, and tested on data from the 2012 and 2013 DIII-D experimental campaigns. It is found that NN can capture the experimental behavior over the majority of the plasma radius and across a broad range of plasma regimes. Although each radial location is calculated independently from the others, the heat flux profiles are smooth, suggesting that the solution found by the NN is a smooth function of the local input parameters. This result supports the evidence of a well-defined, non-stochastic relationship between the input parameters and the experimentally measured transport fluxes. The numerical efficiency of this method, requiring only a few CPU-?s per data point, makes it ideal for scenario development simulations and real-time plasma control.

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

    E-Print Network [OSTI]

    Sridhar, Srinivas

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

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

    SciTech Connect (OSTI)

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

    1993-12-31T23:59:59.000Z

    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.

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. 8. Neural Networks Over the years, linear regression models have attempted to characterise the 0.2% proof stress and

    E-Print Network [OSTI]

    Cambridge, University of

    30 8. Neural Networks Over the years, linear regression models have attempted to characterise the 0 interact. A more powerful alternative is the use of neural networks [40,42], a non-linear modelling prediction uncertainties. #12;31 In linear regression, the sum of each input xi multiplied with a weight wi

  6. 1100 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 5, SEPTEMBER 2004 Temporal Codes and Computations for Sensory

    E-Print Network [OSTI]

    Cariani, Peter

    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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    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

  8. Applications of artificial neural networks in the identification of flow units, Happy Spraberry Field, Garza County, Texas

    E-Print Network [OSTI]

    Gentry, Matthew David

    2005-02-17T23:59:59.000Z

    The use of neural networks in the field of development geology is in its infancy. In this study, a neural network will be used to identify flow units in Happy Spraberry Field, Garza County, Texas. A flow unit is the mappable portion of the total...

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

    E-Print Network [OSTI]

    Gupta, Vinay Kumar

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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

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

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

    SciTech Connect (OSTI)

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

    1990-01-01T23:59:59.000Z

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

  12. Use of neural networks in the operation of nuclear power plants

    SciTech Connect (OSTI)

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

    1990-01-01T23:59:59.000Z

    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.

  13. Tight bounds on the size of neural networks for classification problems

    SciTech Connect (OSTI)

    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

    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.

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

    E-Print Network [OSTI]

    Chou, Chien-Ju

    1993-01-01T23:59:59.000Z

    APPLICATION OF NEURAL NETWORKING IN LIVE CATTLE FUTURES MARKET: AN APPROACH TO PRICE-FORECASTING A Thesis by CHIEN-JU CHOU Submitted to the Office of Graduate Studies of Texas ARM University in partial fulfilhnent of the requirements... for the degree of MASTER OF SCIENCE August 1993 Major Subject: Animal Science APPLICATION OF NEURAL NETWORKING IN LIVE CATTLE FUTURES MARKET: AN APPROACH TO PRICE-FORECASTING A Thesis by CHIBN-JU CHOU Approved as to style and content by: John P. Walter...

  15. Systematic uncertainties of artificial neural-network pulse-shape discrimination for $0\

    E-Print Network [OSTI]

    Abt, I; Cossavella, F; Majorovits, B; Palioselitis, D; Volynets, O

    2014-01-01T23:59:59.000Z

    A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate the systematic uncertainties of the method. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like samples from calibration measurements is estimated to be 5\\%. This uncertainty is due to differences between signal and calibration samples.

  16. 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...

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

    SciTech Connect (OSTI)

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

    2014-07-10T23:59:59.000Z

    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.

  18. Development of Ensemble Neural Network Convection Parameterizations for Climate Models

    SciTech Connect (OSTI)

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

    2012-05-02T23:59:59.000Z

    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.

  19. 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

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

    E-Print Network [OSTI]

    Toinet, Sylvain

    2001-01-01T23:59:59.000Z

    . The solution to this problem was proposed by Minsky and Papert (1969). ' adding a hidden layer of units improves the representation power of a neural network (e. g. increses its number of degrees of freedom). It has been shown by Hartman, Keeler and Kowalski...

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

    E-Print Network [OSTI]

    Anderson, Charles W.

    in sub-optimal control performance. In many situations, the degree of uncertainty in the model the control using neural networks. Section 3 describes how a simple inverse model can be used to train it results in improved performance. 2 Heating Coil Model and PI Control Underwoodand Crawford10 developeda

  2. Phosphene evaluation in a visual prosthesis with artificial neural networks Cdric Archambeau1

    E-Print Network [OSTI]

    Rattray, Magnus

    Phosphene evaluation in a visual prosthesis with artificial neural networks Cédric Archambeau1 of the optic nerve is investigated, as an approach to the development of a microsystems-based visual prosthesis). The development of a visual prosthesis is a project funded by the European Commission (Esprit LTR 22527 "Mivip

  3. Prediction of visual perceptions with artificial neural networks in a visual prosthesis for the blind

    E-Print Network [OSTI]

    Rattray, Magnus

    Prediction of visual perceptions with artificial neural networks in a visual prosthesis. Introduction The European project OPTIVIP (Optimisation of the Visual Implantable Prosthesis) has recently been based visual prosthesis in order to restore partial vision to the blind. In this paper, an attempt

  4. JPEG Quality Transcoding using Neural Networks Trained with a Perceptual Error Measure

    E-Print Network [OSTI]

    Lazzaro, John

    JPEG Quality Transcoding using Neural Networks Trained with a Perceptual Error Measure John Lazzaro@cs.berkeley.edu Abstract A JPEG Quality Transcoder (JQT) converts a JPEG image file that was encoded with low image quality users direct control over the compression process, supporting trade- offs between image quality

  5. Neural Network Controller for Constrained Robot Manipulators Shenghai Hu, Marcelo H. Ang Jr., and H. Krishnan

    E-Print Network [OSTI]

    Ang Jr.,, Marcelo H.

    1 Neural Network Controller for Constrained Robot Manipulators Shenghai Hu, Marcelo H. Ang Jr robot manipulators to a wider class of tasks, it is necessary to control not only the position of a manipulator but also the force exerted by its end-effector on an object or environment. Force control

  6. Sliding Mode Adaptive Neural-Network Control for Nonholonomic Mobile Modular Manipulators

    E-Print Network [OSTI]

    Li, Yangmin

    Sliding Mode Adaptive Neural-Network Control for Nonholonomic Mobile Modular Manipulators YUGANG the dynamic model of the mobile mod- ular manipulator. Sliding mode control and direct adaptive technique modular manipulator, sliding mode control. 1. Introduction In the past decades, modular manipulators have

  7. Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks

    E-Print Network [OSTI]

    Massachusetts at Amherst, University of

    Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural network Quadratic programming Cooperative task execution Redundant manipulator Decentralized kinematic control a b s t r a c t This paper studies the decentralized kinematic control of multiple

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

    E-Print Network [OSTI]

    Portegys, Thomas E.

    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

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

    E-Print Network [OSTI]

    Mak, Man-Wai

    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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    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 compares l'aide de simulations numriques et apparaissent trs similaires. Nos simulations

  11. HIGH ORDER RECURSIVE NEURAL NETWORKS Don Hush, Chaouki Abdallah, and Bill Horne

    E-Print Network [OSTI]

    Department of Electrical Engineering and Computer Engineering University of New Mexico Albuquerque, NM, 87131 USA. ABSTRACT This paper modifies the previously introduced recursive neural net- work (RNN dynamical system. The output of the network is the sum of the outputs of two sub- nets, the nonrecursive

  12. A Fast Learning Strategy Using Pattern Selection for Feedforward Neural Networks

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    . The algorithm builds the training corpus starting from a small randomly chosen initial dataset and new patterns of the hidden units [8]. A fairly new approach considered in the last decade is the reduction of the size-class and inter-class variations. Hence, neural network scheme based approaches are time costly solutions

  13. Forecasting of preprocessed daily solar radiation time series using neural networks

    E-Print Network [OSTI]

    Boyer, Edmond

    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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    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

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

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    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

  16. DeepPose: Human Pose Estimation via Deep Neural Networks Alexander Toshev

    E-Print Network [OSTI]

    Tomkins, Andrew

    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

  17. Neural Network-Based Noise Suppressor and Predictor for Quantifying Valve Stiction in Oscillatory Control Loops

    E-Print Network [OSTI]

    Annan, Carl Ashie

    2014-12-18T23:59:59.000Z

    . This work proposes a neural network approach to quantify the degree of stiction in a valve once the phenomenon has been detected. Several degrees of stiction are simulated in a closed loop control system by specifying the magnitude of static (fs) and dynamic...

  18. Creation and Testing of an Artificial Neural Network Based Carbonate Detector for Mars Rovers

    E-Print Network [OSTI]

    Royer, Dana

    carbonate detector capable of running on current and future rover hardware. The detector can identify result in a critical loss of valuable science data. Onboard techniques for identifying 1 0 neural network (ANN) based carbonate detector capable of running on current and future rover hardware

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

    E-Print Network [OSTI]

    Catholic University of Chile (Universidad Catlica de Chile)

    Energy Management System for an Hybrid Electric Vehicle, Using Ultracapacitors and Neural Networks and specific energy contained in most electric batteries compared to that of gasoline, is resolved in hybrid to accept energy from regenerative braking. For this reason, hybrid systems use an auxiliary energy system

  20. Neural Networks and Machine Learning Solutions of the Exam of 29/1/2007

    E-Print Network [OSTI]

    Almeida, Luis B.

    Neural Networks and Machine Learning Solutions of the Exam of 29/1/2007 Notes: The solutions they solved the problems was right or not. The "carefully justified manner" requested in the exam required as 1 (top) and 2 (bottom), and the unit of the second layer as 3. We shall designate by si and yi

  1. B-spline neural networks based PID controller for Hammerstein systems

    E-Print Network [OSTI]

    Chen, Sheng

    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

  2. Utility based Data Mining for Time Series Analysis -Cost-sensitive Learning for Neural Network Predictors

    E-Print Network [OSTI]

    Weiss, Gary

    Utility based Data Mining for Time Series Analysis - Cost-sensitive Learning for Neural Network@bis-lab.com ABSTRACT In corporate data mining applications, cost-sensitive learning is firmly established Mining General Terms Algorithms, Management, Economics Keywords Data Mining, cost-sensitive learning

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

    E-Print Network [OSTI]

    Coutinho, Alvaro L. G. A.

    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

  4. A Simple Neural Network Models Categorical Perception of Facial Expressions Curtis Padgett and Garrison W. Cottrell

    E-Print Network [OSTI]

    Cottrell, Garrison W.

    facial expressions is compared with human subjects over a set of experiments using interpolated imagery. The experiments for both the human subjects and neural networks make use of in terpolations of facial expressions in materials between those used in the human subjects experiments [Young et al., 1997] and our materials

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

    E-Print Network [OSTI]

    Blekas, Konstantinos

    -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

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

    E-Print Network [OSTI]

    Likas, Aristidis

    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

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

    E-Print Network [OSTI]

    Cambridge, University of

    on experience and test results of creep data obtained after long-term creep tests for several years or moreCREEP STRENGTH OF HIGH CR FERRITIC STEELS DESIGNED USING NEURAL NETWORKS AND PHASE STABILITY of Materials Science and Metallurgy Pembroke Street, Cambridge CB2 3QZ, U.K. Abstract The highest creep rupture

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

    E-Print Network [OSTI]

    Cambridge, University of

    recently analysed how the strength of creepresistant ferritic steels depends on the testrupture provides a ready method for estimating expensive creep data using ordinary tensile tests [1]. AnotherHotStrength of Ferritic CreepResistant Steels Comparison of Neural Network and Genetic

  9. DESIGN OF FUEL-ADDITIVES USING HYBRID NEURAL NETWORKS AND EVOLUTIONARY

    E-Print Network [OSTI]

    Venkatasubramanian, Venkat

    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

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

    E-Print Network [OSTI]

    Weston, Ken

    Prediction of Interface Residues in ProteinProtein 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 proteinprotein complexes. In PPISP, sequence

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

    E-Print Network [OSTI]

    Valkó, Peter

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

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

    E-Print Network [OSTI]

    Fiete, Ila

    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

  13. 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

  14. Neural Network Classifiers and Gene Selection Methods for Microarray Data on Human Lung Adenocarcinoma

    E-Print Network [OSTI]

    Narasimhan, Giri

    Neural Network Classifiers and Gene Selection Methods for Microarray Data on Human Lung with lung cancer gene expression data sets available from the CAMDA website. Many different classification several new methods for classifying gene expression data from lung cancer patients. Our approach uses

  15. NEURAL PCA NETWORK FOR LUNG OUTLINE RECONSTRUCTION IN VQ SCAN IMAGES

    E-Print Network [OSTI]

    Serpen, Gursel

    NEURAL PCA NETWORK FOR LUNG OUTLINE RECONSTRUCTION IN VQ SCAN IMAGES G. Serpen1 , Ph. D., R. Iyer1 system takes the digitized ventilation-perfusion scan images of lungs as input, identify a template according to the size and shape of the lungs and thereby approximate and reconstruct the outline of the lung

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

    E-Print Network [OSTI]

    Widrow, Bernard

    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

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

    SciTech Connect (OSTI)

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

    1991-01-01T23:59:59.000Z

    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.

  18. Neural Network-Based Classification of Single-Phase Distribution Transformer Fault Data

    E-Print Network [OSTI]

    Zhang, Xujia

    2006-08-16T23:59:59.000Z

    an efficient and accurate classification method to separate monitoring signal data into clusters base on their properties. However, raw data collected from the field and simulations will create too many dimensions and inputs to the neural network and make it a...

  19. Iterative prediction of chaotic time series using a recurrent neural network

    SciTech Connect (OSTI)

    Essawy, M.A.; Bodruzzaman, M. [Tennessee State Univ., Nashville, TN (United States). Dept. of Electrical and Computer Engineering; Shamsi, A.; Noel, S. [USDOE Morgantown Energy Technology Center, WV (United States)

    1996-12-31T23:59:59.000Z

    Chaotic systems are known for their unpredictability due to their sensitive dependence on initial conditions. When only time series measurements from such systems are available, neural network based models are preferred due to their simplicity, availability, and robustness. However, the type of neutral network used should be capable of modeling the highly non-linear behavior and the multi-attractor nature of such systems. In this paper the authors use a special type of recurrent neural network called the ``Dynamic System Imitator (DSI)``, that has been proven to be capable of modeling very complex dynamic behaviors. The DSI is a fully recurrent neural network that is specially designed to model a wide variety of dynamic systems. The prediction method presented in this paper is based upon predicting one step ahead in the time series, and using that predicted value to iteratively predict the following steps. This method was applied to chaotic time series generated from the logistic, Henon, and the cubic equations, in addition to experimental pressure drop time series measured from a Fluidized Bed Reactor (FBR), which is known to exhibit chaotic behavior. The time behavior and state space attractor of the actual and network synthetic chaotic time series were analyzed and compared. The correlation dimension and the Kolmogorov entropy for both the original and network synthetic data were computed. They were found to resemble each other, confirming the success of the DSI based chaotic system modeling.

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

    E-Print Network [OSTI]

    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

    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...

  1. Neural Network forecasts of the tropical Pacific sea surface temperatures

    E-Print Network [OSTI]

    Hsieh, William

    . 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

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

    E-Print Network [OSTI]

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

    2006-01-01T23:59:59.000Z

    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...

  3. Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network

    E-Print Network [OSTI]

    Baringer, Philip S.; Bean, Alice; Benelli, Gabriele; Kenny, R. P. III; Murray, Michael J.; Noonan, Danny; Sanders, Stephen J.; Stringer, Robert W.; Tinti, Gemma; Wood, Jeffrey Scott; Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Aguilo, E.; Bergauer, T.; Dragicevic, M.

    2013-04-02T23:59:59.000Z

    In this paper, a search for supersymmetry (SUSY) is presented in events with two opposite-sign isolated leptons in the final state, accompanied by hadronic jets and missing transverse energy. An artificial neural network ...

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

    E-Print Network [OSTI]

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

    2013-07-18T23:59:59.000Z

    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...

  5. Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network

    E-Print Network [OSTI]

    Apyan, Aram

    In this paper, a search for supersymmetry (SUSY) is presented in events with two opposite-sign isolated leptons in the final state, accompanied by hadronic jets and missing transverse energy. An artificial neural network ...

  6. 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

  7. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 9, NO. 5, SEPTEMBER 1998 987 Artificial Neural Networks for Solving Ordinary

    E-Print Network [OSTI]

    Lagaris, Isaac

    essential gains in the execution speed. Index Terms--Collocation method, finite elements, neural net- works finite element method for several cases of partial differential equations. With the advent differential equations (PDE's) (defined on orthogonal box domains) that relies on the function approximation

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

    SciTech Connect (OSTI)

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

    1993-09-01T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    ElSawy, Ismaail; Razek, Mohammed Abdel

    2011-01-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2010-04-12T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bonnett, Christopher

    2013-01-01T23:59:59.000Z

    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...

  12. A new generic approach for optoelectronic hardware realizations of neural networks models

    SciTech Connect (OSTI)

    Agranat, A.; Neugebauer, C.F.; Yariv, A.

    1988-09-01T23:59:59.000Z

    A new generic approach for realizing neural networks (NN) is presented. The underlying principle of the new approach is to take advantage of the fact that signal processing in silicon is an advanced and mature technology, and to incorporate optics where silicon fails, namely in the interconnectivity problem. The basic idea is described. The system consists of two main subassemblies: a 2D Spatial Light Modulator (SLM) and an integrated circuit to which the authors shall henceforth refer to as the Neural Processor (NP). The synaptic efficacies matrix W is stored in the SLM. Thus by imaging the SLM contents onto an array detector which serves as the input unit of the NP, W is loaded in parallel into the NP. The NP then updates the state of the network in parallel/semiparallel-synchronous/asynchronous manner (depending on the structure of the NP).

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

    SciTech Connect (OSTI)

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

    2011-05-20T23:59:59.000Z

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

  14. Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

    E-Print Network [OSTI]

    Chattopadhyay, Goutami; 10.1140/epjp/i2012-12043-9

    2012-01-01T23:59:59.000Z

    This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).

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

    E-Print Network [OSTI]

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

    2011-10-21T23:59:59.000Z

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

  16. Artificial neural networks for centroiding elongated spots in Shack-Hartmann wavefront sensors

    E-Print Network [OSTI]

    Mello, A T; Guzman, D; Guesalaga, A

    2014-01-01T23:59:59.000Z

    The use of Adaptive Optics in Extremely Large Telescopes brings new challenges, one of which is the treatment of Shack-Hartmann Wavefront sensors images. When using this type of sensors in conjunction with laser guide stars for sampling the pupil of telescopes with 30+ m in diameter, it is necessary to compute the centroid of elongated spots, whose elongation angle and aspect ratio are changing across the telescope pupil. Existing techniques such as Matched Filter have been considered as the best technique to compute the centroid of elongated spots, however they are not good at coping with the effect of a variation in the Sodium profile. In this work we propose a new technique using artificial neural networks, which take advantage of the neural network's ability to cope with changing conditions, outperforming existing techniques in this context. We have developed comprehensive simulations to explore this technique and compare it with existing algorithms.

  17. Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

    E-Print Network [OSTI]

    Goutami Chattopadhyay; Surajit Chattopadhyay

    2012-04-18T23:59:59.000Z

    This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).

  18. Hybrid Neural Systems Stefan Wermter

    E-Print Network [OSTI]

    Varela, Carlos

    : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 108 Stefan C. Kremer and John Kolen Fuzzy knowledge and recurrent neural networks: A dy- namical

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

    E-Print Network [OSTI]

    Johnson, M. L.

    1998-01-01T23:59:59.000Z

    ., April 1996. 5. Ungar, Hartman, Eric J, Keeler, James D. and Martin Greg D. Process Modeling and Control Using Neural Networks. Intelligent Systems in Process Engineering Conference Proceedings. July 1995 6. Lewis, Mike. 1996. The Lower Colorado.... Keeler, Jim, Havener H, Hartman, E. and Magnuson T. 1993. Achieving Compliance and Profits with a Predictive Emission Monitoring System: Pavilion's Software CEMTM. Pavilion Technologies, Inc., 9. Department of Energy Fact Sheet. 1994. More...

  20. Short-term load forecasting using generalized regression and probabilistic neural networks in the electricity market

    SciTech Connect (OSTI)

    Tripathi, M.M.; Upadhyay, K.G.; Singh, S.N.

    2008-11-15T23:59:59.000Z

    For the economic and secure operation of power systems, a precise short-term load forecasting technique is essential. Modern load forecasting techniques - especially artificial neural network methods - are particularly attractive, as they have the ability to handle the non-linear relationships between load, weather temperature, and the factors affecting them directly. A test of two different ANN models on data from Australia's Victoria market is promising. (author)

  1. Artificial Neural Networks as Non-Linear Extensions of Statistical Methods in Astronomy

    E-Print Network [OSTI]

    Ofer Lahav

    1994-11-17T23:59:59.000Z

    We attempt to de-mistify Artificial Neural Networks (ANNs) by considering special cases which are related to other statistical methods common in Astronomy and other fields. In particular we show how ANNs generalise Bayesian methods, multi-parameter fitting, Principal Component Analysis (PCA), Wiener filtering and regularisation methods. Examples of morphological classification of galaxies illustrate how non-linear ANNs improve on linear techniques.

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

    DOE Patents [OSTI]

    Fu, C.Y.

    1998-11-24T23:59:59.000Z

    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.

  3. The High Time Resolution Universe Survey VI: An Artificial Neural Network and Timing of 75 Pulsars

    E-Print Network [OSTI]

    Bates, S D; Barsdell, B R; Bhat, N D R; Burgay, M; Burke-Spolaor, S; Champion, D J; Coster, P; D'Amico, N; Jameson, A; Johnston, S; Keith, M J; Kramer, M; Levin, L; Lyne, A; Milia, S; Ng, C; Nietner, C; Possenti, A; Stappers, B; Thornton, D; van Straten, W

    2012-01-01T23:59:59.000Z

    We present 75 pulsars discovered in the mid-latitude portion of the High Time Resolution Universe survey, 54 of which have full timing solutions. All the pulsars have spin periods greater than 100 ms, and none of those with timing solutions are in binaries. Two display particularly interesting behaviour; PSR J1054-5944 is found to be an intermittent pulsar, and PSR J1809-0119 has glitched twice since its discovery. In the second half of the paper we discuss the development and application of an artificial neural network in the data-processing pipeline for the survey. We discuss the tests that were used to generate scores and find that our neural network was able to reject over 99% of the candidates produced in the data processing, and able to blindly detect 85% of pulsars. We suggest that improvements to the accuracy should be possible if further care is taken when training an artificial neural network; for example ensuring that a representative sample of the pulsar population is used during the training proc...

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

    SciTech Connect (OSTI)

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

    2006-08-29T23:59:59.000Z

    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.

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

    DOE Patents [OSTI]

    Fu, Chi Yung (San Francisco, CA)

    1998-01-01T23:59:59.000Z

    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.

  6. artificial neural network-based: Topics by E-print Network

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

    be separated from back- ground by thresholding In this paper, a computer vision based system is introduced to automatically sort apple fruits. An artificial neural net- work...

  7. Seismic Facies Classification And Identification By Competitive Neural Networks

    E-Print Network [OSTI]

    Saggaf, Muhammad M.

    2000-01-01T23:59:59.000Z

    We present an approach based on competitive networks for the classification and identification of reservoir facies from seismic data. This approach can be adapted to perform either classification of the seismic facies based ...

  8. Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks

    E-Print Network [OSTI]

    Nicholas M Ball; Jon Loveday; Masataka Fukugita; Osamu Nakamura; Sadanori Okamura; Jon Brinkmann; Robert J Brunner

    2003-06-19T23:59:59.000Z

    Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen data, it is found that correlations between predicted and actual properties are around 0.9 with rms errors of order ten per cent. Thus, given a representative training set, these properties may be reliably estimated for galaxies in the survey for which there are no spectra and without human intervention.

  9. Stochastic mean field formulation of the dynamics of diluted neural networks

    E-Print Network [OSTI]

    D. Angulo-Garcia; A. Torcini

    2014-09-26T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

    Amerio, Silvia; /Trento U.

    2005-12-01T23:59:59.000Z

    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.

  11. 746 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 44, NO. 6, DECEMBER 1997 Adaptive Neural Network Control of

    E-Print Network [OSTI]

    Ge, Shuzhi Sam

    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

  12. Stabilizing and Robustifying the Learning Mechanisms of Artificial Neural Networks

    E-Print Network [OSTI]

    Efe, Mehmet ?nder

    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

  13. in Proceedings of the 1999 International Joint Conference on Neural Network (pp. 305i305vi) 305i When local isn't enough: Extracting distributed rules from networks

    E-Print Network [OSTI]

    Dawson, Michael

    When local isn't enough: Extracting distributed rules from networks David A. Medler Center nature of neural networks. In this paper we discuss a technique for ex- tracting distributed symbolicin Proceedings of the 1999 International Joint Conference on Neural Network (pp. 305i305vi) 305i

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

    SciTech Connect (OSTI)

    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

    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.

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

    SciTech Connect (OSTI)

    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

    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.

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

    SciTech Connect (OSTI)

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

    1993-10-01T23:59:59.000Z

    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.

  17. 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]

    Paris-Sud XI, Universit de

    2003-01-01T23:59:59.000Z

    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

  18. Simulation of flood flow in a river system using artificial neural networks Hydrology and Earth System Sciences, 9(4), 313321 (2005) EGU

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    2005-01-01T23:59:59.000Z

    Simulation of flood flow in a river system using artificial neural networks 313 Hydrology and Earth System Sciences, 9(4), 313321 (2005) EGU Simulation of flood flow in a river system using artificial Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation

  19. Chai, S.S., Veenendaal,B., West G. and J.P. Walker (2009). Input Parameter Selection for Soil Moisture Retrieval Using an Artificial Neural Network. In: Ostendorf, B., Baldock, P., Bruce, D., Burdett, M. and P. Corcoran (eds.),

    E-Print Network [OSTI]

    Walker, Jeff

    2009-01-01T23:59:59.000Z

    Moisture Retrieval Using an Artificial Neural Network. In: Ostendorf, B., Baldock, P., Bruce, D., Burdett-0-9581366-8-6. INPUT PARAMETERS SELECTION FOR SOIL MOISTURE RETRIEVAL USING AN ARTIFICIAL NEURAL NETWORK Soo-See Chai 1-linear and ill-posed problem. Artificial Neural Networks (ANNs) have been demonstrated to be good solutions

  20. ESANN'1993 proceedings -European Symposium on Artificial Neural Networks Brussels (Belgium), 7-8-9 April 1993, D-Facto public., ISBN 2-9600049-0-6, pp. 209-214

    E-Print Network [OSTI]

    Verleysen, Michel

    ESANN'1993 proceedings - European Symposium on Artificial Neural Networks Brussels (Belgium), 7 Symposium on Artificial Neural Networks Brussels (Belgium), 7-8-9 April 1993, D-Facto public., ISBN 2-9600049-0-6, pp. 209-214 #12;ESANN'1993 proceedings - European Symposium on Artificial Neural Networks Brussels

  1. ESANN'1994 proceedings -European Symposium on Artificial Neural Networks Brussels (Belgium), 20-21-22 April 1994, D-Facto public., ISBN 2-9600049-1-4, pp. 37-42

    E-Print Network [OSTI]

    Verleysen, Michel

    ESANN'1994 proceedings - European Symposium on Artificial Neural Networks Brussels (Belgium), 20 Symposium on Artificial Neural Networks Brussels (Belgium), 20-21-22 April 1994, D-Facto public., ISBN 2-9600049-1-4, pp. 37-42 #12;ESANN'1994 proceedings - European Symposium on Artificial Neural Networks Brussels

  2. ESANN'1998 proceedings -European Symposium on Artificial Neural Networks Bruges (Belgium), 22-23-24 April 1998, D-Facto public., ISBN 2-9600049-8-1, pp. 91-98

    E-Print Network [OSTI]

    Schierwagen, Andreas

    ESANN'1998 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 22 Symposium on Artificial Neural Networks Bruges (Belgium), 22-23-24 April 1998, D-Facto public., ISBN 2-9600049-8-1, pp. 91-98 #12;ESANN'1998 proceedings - European Symposium on Artificial Neural Networks Bruges

  3. ESANN'1995 proceedings -European Symposium on Artificial Neural Networks Brussels (Belgium), 19-20-21 April 1995, D-Facto public., ISBN 2-9600049-3-0, pp. 153-160

    E-Print Network [OSTI]

    Verleysen, Michel

    ESANN'1995 proceedings - European Symposium on Artificial Neural Networks Brussels (Belgium), 19 Symposium on Artificial Neural Networks Brussels (Belgium), 19-20-21 April 1995, D-Facto public., ISBN 2-9600049-3-0, pp. 153-160 #12;ESANN'1995 proceedings - European Symposium on Artificial Neural Networks Brussels

  4. ESANN'1995 proceedings -European Symposium on Artificial Neural Networks Brussels (Belgium), 19-20-21 April 1995, D-Facto public., ISBN 2-9600049-3-0, pp. 365-371

    E-Print Network [OSTI]

    Avignon et des Pays de Vaucluse, Universit de

    ESANN'1995 proceedings - European Symposium on Artificial Neural Networks Brussels (Belgium), 19 Symposium on Artificial Neural Networks Brussels (Belgium), 19-20-21 April 1995, D-Facto public., ISBN 2-9600049-3-0, pp. 365-371 #12;ESANN'1995 proceedings - European Symposium on Artificial Neural Networks Brussels

  5. ESANN'1993 proceedings -European Symposium on Artificial Neural Networks Brussels (Belgium), 7-8-9 April 1993, D-Facto public., ISBN 2-9600049-0-6, pp. 137-144

    E-Print Network [OSTI]

    Verleysen, Michel

    ESANN'1993 proceedings - European Symposium on Artificial Neural Networks Brussels (Belgium), 7 Symposium on Artificial Neural Networks Brussels (Belgium), 7-8-9 April 1993, D-Facto public., ISBN 2-9600049-0-6, pp. 137-144 #12;ESANN'1993 proceedings - European Symposium on Artificial Neural Networks Brussels

  6. Nuclear power plant transient diagnostics using artificial neural networks that allow ``don`t-know`` classifications

    SciTech Connect (OSTI)

    Bartal, Y.; Lin, J.; Uhrig, R.E. [Oak Ridge National Lab., TN (United States). Instrumentation and Controls Div.

    1995-06-01T23:59:59.000Z

    A nuclear power plant`s (NPP`s) status is usually monitored by a human operator. Any classifier system used to enhance the operator`s capability to diagnose a safety-critical system like an NPP should classify a novel transient as ``don`t-know`` if it is not contained within its accumulated knowledge base. In particular, the classifier needs some kind of proximity measure between the new data and its training set. Artificial neural networks have been proposed as NPP classifiers, the most popular ones being the multilayered perceptron (MLP) type. However, MLPs do not have a proximity measure, while learning vector quantization, probabilistic neural networks (PNNs), and some others do. This proximity measure may also serve as an explanation to the classifier`s decision in the way that case-based-reasoning expert systems do. The capability of a PNN network as a classifier is demonstrated using simulator data for the three-loop 436-MW(electric) Westinghouse San Onofre unit 1 pressurized water reactor. A transient`s classification history is used in an ``evidence accumulation`` technique to enhance a classifier`s accuracy as well as its consistency.

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

    SciTech Connect (OSTI)

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

    1990-12-31T23:59:59.000Z

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

  8. Iterative prediction of chaotic time series using a recurrent neural network. Quarterly progress report, January 1, 1995--March 31, 1995

    SciTech Connect (OSTI)

    Bodruzzaman, M.; Essawy, M.A.

    1996-03-31T23:59:59.000Z

    Chaotic systems are known for their unpredictability due to their sensitive dependence on initial conditions. When only time series measurements from such systems are available, neural network based models are preferred due to their simplicity, availability, and robustness. However, the type of neural network used should be capable of modeling the highly non-linear behavior and the multi- attractor nature of such systems. In this paper we use a special type of recurrent neural network called the ``Dynamic System Imitator (DSI)``, that has been proven to be capable of modeling very complex dynamic behaviors. The DSI is a fully recurrent neural network that is specially designed to model a wide variety of dynamic systems. The prediction method presented in this paper is based upon predicting one step ahead in the time series, and using that predicted value to iteratively predict the following steps. This method was applied to chaotic time series generated from the logistic, Henon, and the cubic equations, in addition to experimental pressure drop time series measured from a Fluidized Bed Reactor (FBR), which is known to exhibit chaotic behavior. The time behavior and state space attractor of the actual and network synthetic chaotic time series were analyzed and compared. The correlation dimension and the Kolmogorov entropy for both the original and network synthetic data were computed. They were found to resemble each other, confirming the success of the DSI based chaotic system modeling.

  9. Advanced microprocessor based power protection system using artificial neural network techniques

    SciTech Connect (OSTI)

    Chen, Z.; Kalam, A.; Zayegh, A. [Victoria Univ. of Technology, Melbourne , Victoria (Australia). Save Energy Research Group

    1995-12-31T23:59:59.000Z

    This paper describes an intelligent embedded microprocessor based system for fault classification in power system protection system using advanced 32-bit microprocessor technology. The paper demonstrates the development of protective relay to provide overcurrent protection schemes for fault detection. It also describes a method for power fault classification in three-phase system based on the use of neural network technology. The proposed design is implemented and tested on a single line three phase power system in power laboratory. Both the hardware and software development are described in detail.

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

    SciTech Connect (OSTI)

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

    1988-01-01T23:59:59.000Z

    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.

  11. Using neural networks as an event trigger in elementary particle physics experiments

    SciTech Connect (OSTI)

    Neis, E.; Starr, F.W.; Handler, T. [Tennessee Univ., Knoxville, TN (United States). Dept. of Physics; Gabriel, T.; Glover, C.; Saini, S. [Oak Ridge National Lab., TN (United States)

    1994-02-01T23:59:59.000Z

    Elementary particle physics experiments often have to deal with high data rates. In order to avoid having to write out all data that is occurring online processors, triggers, are used to cull out the uninteresting data. These triggers are based on some particular aspect of the physics being examined. At times these aspects are often equivalent to simple pattern recognition problems. The reliability of artificial neural networks(ANNs) in pattern recognition problems in many fields has been well demonstrated. We present here the results of a study on the feasibility of using ANNs as an online trigger for high energy physics experiments.

  12. Automated Classification of Sloan Digital Sky Survey (SDSS) Stellar Spectra using Artificial Neural Networks

    E-Print Network [OSTI]

    Mahdi Bazarghan; Ranjan Gupta

    2008-04-26T23:59:59.000Z

    Automated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated techniques for analysis of such large datasets which are now available to the community. Sloan Digital Sky Survey (SDSS) is one of such surveys releasing massive datasets. We use Probabilistic Neural Network (PNN) for automatic classification of about 5000 SDSS spectra into 158 spectral type of a reference library ranging from O type to M type stars.

  13. The Artificial Neural Networks as a tool for analysis of the individual Extensive Air Showers data

    E-Print Network [OSTI]

    Tadeusz Wibig

    1996-08-03T23:59:59.000Z

    In that paper we discuss possibilities of using the Artificial Neural Network technic for the individual Extensive Air Showers data evaluation. It is shown that the recently developed new computational methods can be used in studies of EAS registered by very large and complex detector systems. The ANN can be used to classify showers due to e.g. primary particle mass as well as to find a particular EAS parameter like e.g. total muon number. The examples of both kinds of analysis are given and discussed.

  14. Systematics on ground-state energies of nuclei within the neural networks

    E-Print Network [OSTI]

    Tuncay Bayram; Serkan Akkoyun; S. Okan Kara

    2013-01-11T23:59:59.000Z

    One of the fundamental ground-state properties of nuclei is binding energy. In this study, we have employed artificial neural networks (ANNs) to obtain binding energies based on the data calculated from Hartree-Fock-Bogolibov (HFB) method with the two SLy4 and SKP Skyrme forces. Also, ANNs have been employed to obtain two-neutron and two-proton separation energies of nuclei. Statistical modeling of nuclear data using ANNs has been seen as to be successful in this study. Such a statistical model can be possible tool for searching in systematics of nuclei beyond existing experimental nuclear data.

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

    SciTech Connect (OSTI)

    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

    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.

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

    E-Print Network [OSTI]

    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

    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.

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

    SciTech Connect (OSTI)

    Kurt Derr; Milos Manic

    2008-06-01T23:59:59.000Z

    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.

  18. Time-of-flight discrimination between gamma-rays and neutrons by using artificial neural networks

    E-Print Network [OSTI]

    Akkoyun, Serkan

    2012-01-01T23:59:59.000Z

    The gamma-ray tracking detector arrays, such as advanced gamma ray tracking array (AGATA), are quite powerful detection systems in nuclear structure physic studies. In these arrays, the sequences of the gamma-ray interaction points in the detectors can correctly be identified in order to obtain true gamma-ray energies emitted from the nuclei of interest. Together with the gamma-rays, a number of neutrons are also emitted from the nuclei and these neutrons influence gamma-ray spectra. An obvious method of separating between neutrons and gamma-rays is based on the time-of-flight (tof) technique. This work aims obtaining tof distributions of gamma-rays and neutrons by using feed-forward artificial neural network (ANN). It was shown that, ANN can correctly classify gamma-ray and neutron events. Testing of trained networks on experimental data clearly shows up tof discrimination of gamma-rays and neutrons.

  19. Neural network system and methods for analysis of organic materials and structures using spectral data

    DOE Patents [OSTI]

    Meyer, Bernd J. (Athens, GA); Sellers, Jeffrey P. (Suwanee, GA); Thomsen, Jan U. (Fredricksberg, DK)

    1993-01-01T23:59:59.000Z

    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.

  20. Neural network system and methods for analysis of organic materials and structures using spectral data

    DOE Patents [OSTI]

    Meyer, B.J.; Sellers, J.P.; Thomsen, J.U.

    1993-06-08T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Cofre, Rodrigo

    2014-01-01T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

    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

    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.

  3. Application of artificial neural networks in power system security and vulnerability assessment

    SciTech Connect (OSTI)

    Qin Zhou; Davidson, J.; Fouad, A.A.

    1994-02-01T23:59:59.000Z

    In a companion paper the concept of system vulnerability is introduced as a new framework for power system dynamic security assessment. Using the TEF method of transient stability analysis, the energy margin [Delta]V is used as an indicator of the level of security, and its sensitivity to a changing system parameter p ([partial derivative][Delta]V/[partial derivative]p) as indicator of its trend with changing system conditions. These two indicators are combined to determine the degree of system vulnerability to contingent disturbances in a stability-limited power system. Thresholds for acceptable levels of the security indicator and its trend are related to the stability limits of a critical system parameter (plant generation limits). Operating practices and policies are used to determine these thresholds. In this paper the artificial neural networks (ANNs) technique is applied to the concept of system vulnerability within the recently developed framework, for fast pattern recognition and classification of system dynamic security status. A suitable topology for the neural network is developed, and the appropriate training method and input and output signals are selected. The procedure developed is successfully applied to the IEEE 50-generator test system. Data previously obtained by heuristic techniques are used for training the ANN.

  4. Towards a feasible implementation of quantum neural networks using quantum dots

    E-Print Network [OSTI]

    M. V. Altaisky; N. N. Zolnikova; N. E. Kaputkina; V. A. Krylov; Yu. E. Lozovik; N. S. Dattani

    2015-03-17T23:59:59.000Z

    We propose an implementation of quantum neural networks using an array of single-electron quantum dots with dipole-dipole interactions. We demonstrate that this implementation is both feasible and versatile by studying it within the framework of GaAs based quantum dot qubits coupled to a reservoir of acoustic phonons; a system whose decoherence properties have been experimentally and theoretically characterized with meticulous detail, and is considered one of the most accurately understood open quantum systems. Using numerically exact Feynman integral calculations, we have found that the quantum coherence in our neural networks survive for several ns even at liquid nitrogen temperatures (77 K), which is three orders of magnitude higher than current implementations which are based on SQUIDs operating at temperatures in the mK range. Furthermore, the previous quantum dot based proposals required control via manipulating the phonon bath, which is extremely difficult in real experiments. An advantage of our implementation is that it can be easily controlled, since dipole-dipole interaction strengths can be changed via the spacing between the dots and applying external fields.

  5. Forecasting of Indian Rupee (INR) / US Dollar (USD) Currency Exchange Rate Using Artificial Neural Network

    E-Print Network [OSTI]

    Perwej, Yusuf; 10.5121/ijcsea.2012.2204

    2012-01-01T23:59:59.000Z

    A large part of the workforce, and growing every day, is originally from India. India one of the second largest populations in the world, they have a lot to offer in terms of jobs. The sheer number of IT workers makes them a formidable travelling force as well, easily picking up employment in English speaking countries. The beginning of the economic crises since 2008 September, many Indians have return homeland, and this has had a substantial impression on the Indian Rupee (INR) as liken to the US Dollar (USD). We are using numerational knowledge based techniques for forecasting has been proved highly successful in present time. The purpose of this paper is to examine the effects of several important neural network factors on model fitting and forecasting the behaviours. In this paper, Artificial Neural Network has successfully been used for exchange rate forecasting. This paper examines the effects of the number of inputs and hidden nodes and the size of the training sample on the in-sample and out-of-sample...

  6. Can Artificial Neural Networks be Applied in Seismic Predicition? Preliminary Analysis Applying Radial Topology. Case: Mexico

    E-Print Network [OSTI]

    Mota-Hernandez, Cinthya; Alvarado-Corona, Rafael

    2014-01-01T23:59:59.000Z

    Tectonic earthquakes of high magnitude can cause considerable losses in terms of human lives, economic and infrastructure, among others. According to an evaluation published by the U.S. Geological Survey, 30 is the number of earthquakes which have greatly impacted Mexico from the end of the XIX century to this one. Based upon data from the National Seismological Service, on the period between January 1, 2006 and May 1, 2013 there have occurred 5,826 earthquakes which magnitude has been greater than 4.0 degrees on the Richter magnitude scale (25.54% of the total of earthquakes registered on the national territory), being the Pacific Plate and the Cocos Plate the most important ones. This document describes the development of an Artificial Neural Network (ANN) based on the radial topology which seeks to generate a prediction with an error margin lower than 20% which can inform about the probability of a future earthquake one of the main questions is: can artificial neural networks be applied in seismic forecast...

  7. Consistent empirical physical formula construction for recoil energy distribution in HPGe detectors using artificial neural networks

    E-Print Network [OSTI]

    Serkan Akkoyun; Nihat Yildiz

    2012-07-23T23:59:59.000Z

    The gamma-ray tracking technique is one of the highly efficient detection method in experimental nuclear structure physics. On the basis of this method, two gamma-ray tracking arrays, AGATA in Europe and GRETA in the USA, are currently being developed. The interactions of neutrons in these detectors lead to an unwanted background in the gamma-ray spectra. Thus, the interaction points of neutrons in these detectors have to be determined in the gamma-ray tracking process in order to improve photo-peak efficiencies and peak-to-total ratios of the gamma-ray peaks. Therefore, the recoil energy distributions of germanium nuclei due to inelastic scatterings of 1-5 MeV neutrons were obtained both experimentally and using artificial neural networks. Also, for highly nonlinear detector response for recoiling germanium nuclei, we have constructed consistent empirical physical formulas (EPFs) by appropriate layered feed-forward neural networks (LFNNs). These LFNN-EPFs can be used to derive further physical functions which could be relevant to determination of neutron interactions in gamma-ray tracking process.

  8. Polymer property prediction and optimization using neural networks Nilay K. Roy, Walter D. Potter, and David P. Landau

    E-Print Network [OSTI]

    Potter, Don

    Polymer property prediction and optimization using neural networks Nilay K. Roy, Walter D. Potter, and David P. Landau Abstract -- Prediction and optimization of polymer properties is a complex and highly-transition, polymerization, modulus. Manuscript submitted September 28, 2004. Dr. Roy is a Postdoctoral Research Fellow

  9. Short-term Wind Power Prediction for Offshore Wind Farms -Evaluation of Fuzzy-Neural Network Based Models

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    Short-term Wind Power Prediction for Offshore Wind Farms - Evaluation of Fuzzy-Neural Network Based of offshore farms and their secure integration to the grid. Modeling the behavior of large wind farms presents the new considerations that have to be made when dealing with large offshore wind farms

  10. 170 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 45, NO. 1, FEBRUARY 1998 Application of Functional Link Neural Network to

    E-Print Network [OSTI]

    Chow, Mo-Yuen

    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

  11. An Evolutionary Race: A Comparison of Genetic Algorithms and Particle Swarm Optimization Used for Training Neural Networks

    E-Print Network [OSTI]

    White, Tony

    with infrared distance sensors. A more detailed physics model would be needed, but the neural network training period of time. Each car is mounted with multiple straight-line distance sensors, which provide the input traveled and rely on this for fitness evaluations. Both evolutionary algorithms are well suited

  12. Assessment of Individual Risk of Death Using Self-report Data: an Artificial Neural Network Compared to a Frailty Index

    E-Print Network [OSTI]

    Mitnitski, Arnold B.

    1 Assessment of Individual Risk of Death Using Self-report Data: an Artificial Neural Network,4 and Kenneth Rockwood, MD1,4 1 Geriatric Medicine Research Unit, Queen Elizabeth II Health Sciences Centre rate over 10 simulations in predicting the probability of individual survival was 79.2 0

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

    E-Print Network [OSTI]

    Mitra, Sushmita

    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

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

    E-Print Network [OSTI]

    Maclin, Rich

    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

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

    E-Print Network [OSTI]

    Stengel, Robert F.

    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

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

    E-Print Network [OSTI]

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

    2006-01-01T23:59:59.000Z

    is proposed based on the RBF neural network with the associated parameters of sample deviation and partial sample deviation, which are defined for the purpose of effective judgment of new samples. Also, in order to forecast the load of sample with large...

  17. Predicting Well Stimulation Results in a Gas Storage Field in the Absence of Reservoir Data, Using Neural Networks

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    SPE 31159 Predicting Well Stimulation Results in a Gas Storage Field in the Absence of Reservoir Data, Using Neural Networks Mohaghegh, S., West Virginia University, McVey, D., National Gas and Oil for presentation by an SPE Program Committee following review of date wells with the highest potential

  18. Ennett CM, Frize M. An investigation into the strengths and limitations of artificial neural networks: an application to an adult ICU patient database. Proc AMIA Symp 1998:998.

    E-Print Network [OSTI]

    Frize, Monique

    into the Strengths and Limitations of Artificial Neural Networks: An Application to an Adult ICU Patient Database The objective was to determine the optimal operating conditions for an artificial neural network (ANNEnnett CM, Frize M. An investigation into the strengths and limitations of artificial neural

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

    SciTech Connect (OSTI)

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

    2011-12-15T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

    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

    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.

  1. Estimation of soil moisture in paddy field using Artificial Neural Networks

    E-Print Network [OSTI]

    Arif, Chusnul; Setiawan, Budi Indra; Doi, Ryoichi

    2013-01-01T23:59:59.000Z

    In paddy field, monitoring soil moisture is required for irrigation scheduling and water resource allocation, management and planning. The current study proposes an Artificial Neural Networks (ANN) model to estimate soil moisture in paddy field with limited meteorological data. Dynamic of ANN model was adopted to estimate soil moisture with the inputs of reference evapotranspiration (ETo) and precipitation. ETo was firstly estimated using the maximum, average and minimum values of air temperature as the inputs of model. The models were performed under different weather conditions between the two paddy cultivation periods. Training process of model was carried out using the observation data in the first period, while validation process was conducted based on the observation data in the second period. Dynamic of ANN model estimated soil moisture with R2 values of 0.80 and 0.73 for training and validation processes, respectively, indicated that tight linear correlations between observed and estimated values of s...

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

    SciTech Connect (OSTI)

    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

    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.

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

    SciTech Connect (OSTI)

    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

    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, 291303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415432 (2008)] and Chen et al. [Sci. China Math. 56(9), 18691878 (2013)]. We also give some numeric simulations to verify our theoretical results.

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

    SciTech Connect (OSTI)

    NONE

    1995-09-16T23:59:59.000Z

    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.

  5. Dynamic modeling of physical phenomena for probabilistic risk assessments using artificial neural networks

    SciTech Connect (OSTI)

    Benjamin, A.S.; Paez, T.L.; Brown, N.N.

    1998-01-01T23:59:59.000Z

    In most probabilistic risk assessments, there is a subset of accident scenarios that involves physical challenges to the system, such as high heat rates and/or accelerations. The system`s responses to these challenges may be complicated, and their prediction may require the use of long-running computer codes. To deal with the many scenarios demanded by a risk assessment, the authors have been investigating the use of artificial neural networks (ANNs) as a fast-running estimation tool. They have developed a multivariate linear spline algorithm by extending previous ANN methods that use radial basis functions. They have applied the algorithm to problems involving fires, shocks, and vibrations. They have found that within the parameter range for which it is trained, the algorithm can simulate the nonlinear responses of complex systems with high accuracy. Running times per case are less than one second.

  6. Automated Classification of ELODIE Stellar Spectral Library Using Probabilistic Artificial Neural Networks

    E-Print Network [OSTI]

    Bazarghan, Mahdi

    2008-01-01T23:59:59.000Z

    A Probabilistic Neural Network model has been used for automated classification of ELODIE stellar spectral library consisting of about 2000 spectra into 158 known spectro-luminosity classes. The full spectra with 561 flux bins and a PCA reduced set of 57, 26 and 16 components have been used for the training and test sessions. The results shows a spectral type classification accuracy of 3.2 sub-spectral type and luminosity class accuracy of 2.7 for the full spectra and an accuracy of 3.1 and 2.6 respectively with the PCA set. This technique will be useful for future upcoming large data bases and their rapid classification.

  7. Automated Classification of ELODIE Stellar Spectral Library Using Probabilistic Artificial Neural Networks

    E-Print Network [OSTI]

    Mahdi Bazarghan

    2008-04-17T23:59:59.000Z

    A Probabilistic Neural Network model has been used for automated classification of ELODIE stellar spectral library consisting of about 2000 spectra into 158 known spectro-luminosity classes. The full spectra with 561 flux bins and a PCA reduced set of 57, 26 and 16 components have been used for the training and test sessions. The results shows a spectral type classification accuracy of 3.2 sub-spectral type and luminosity class accuracy of 2.7 for the full spectra and an accuracy of 3.1 and 2.6 respectively with the PCA set. This technique will be useful for future upcoming large data bases and their rapid classification.

  8. Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation

    E-Print Network [OSTI]

    Cintra, Rosangela S

    2014-01-01T23:59:59.000Z

    This paper presents an approach for employing artificial neural networks (NN) to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation. The assimilation methods are tested in the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localization of balloon soundings. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the analysis from the local ensemble transform Kalman filter (LETKF). After the training process, the method using the MLP-NN is seen as a function of data assimilation. The NN were trained with data from first three months of 1982, 1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle using MLP-NN were performed with synthetic observations for January 1985. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilati...

  9. Hybrid expert system - neural network - Fuzzy Logic methodology for transient identification

    SciTech Connect (OSTI)

    Ikonomopoulos, A.; Tsoukalas, L.H. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering; Uhrig, R.E. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering]|[Oak Ridge National Lab., TN (United States)

    1991-12-31T23:59:59.000Z

    A methodology is presented that demonstrates the potential of pretrained artificial neural networks (ANN`s) as generators of membership functions for the purpose of transient identification in Nuclear Power Plants (NPP). In order to provide timely concise and task-specific information about the many aspects of the transient and to determine the state of the system based on the interpretation of potentially noisy data, a model-referenced approach is utilized, where pretrained ANNs provide the model. Membership functions -- that condense information about a transient in a form convenient for a rule-based identification system -- are produced through ANN`S. The results demonstrate the extremely good noise-tolerance of ANN`s and suggest a new method for transient identification within the framework of Fuzzy Logic.

  10. Hybrid expert system - neural network - Fuzzy Logic methodology for transient identification

    SciTech Connect (OSTI)

    Ikonomopoulos, A.; Tsoukalas, L.H. (Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering); Uhrig, R.E. (Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering Oak Ridge National Lab., TN (United States))

    1991-01-01T23:59:59.000Z

    A methodology is presented that demonstrates the potential of pretrained artificial neural networks (ANN's) as generators of membership functions for the purpose of transient identification in Nuclear Power Plants (NPP). In order to provide timely concise and task-specific information about the many aspects of the transient and to determine the state of the system based on the interpretation of potentially noisy data, a model-referenced approach is utilized, where pretrained ANNs provide the model. Membership functions -- that condense information about a transient in a form convenient for a rule-based identification system -- are produced through ANN'S. The results demonstrate the extremely good noise-tolerance of ANN's and suggest a new method for transient identification within the framework of Fuzzy Logic.

  11. artificially generated gravity: Topics by E-print Network

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

    (Nolfi and Floreano, 2000), neural networks design files of body com- ponents for 3D printing, and for compiling neural-network controllers to run artificial neural networks....

  12. 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]

    Hasanhodzic, Jasmina, 1979-

    2004-01-01T23:59:59.000Z

    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 ...

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

    E-Print Network [OSTI]

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

    2004-01-01T23:59:59.000Z

    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...

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

    E-Print Network [OSTI]

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

    2004-01-01T23:59:59.000Z

    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...

  15. This is the documentation for the UTS neural network, as described in chapter 8. Program MAP_NEURAL_AUSTENITIC_UTS

    E-Print Network [OSTI]

    Cambridge, University of

    composition and heat treatments. Specification Language: FORTRAN / C Product form: Source code / Executable files Operating System: Solaris 5.5.1 & Windows 95 Description The modelling procedure is a purelyKay and is part of the bigback5 program. The model is constituted of a committee of several individual neural

  16. 846 IEEE TRANSACTIONS ON NEURAL NETWORKS ON LEARNING SYSTEMS, VOL. 23, NO. 5, MAY 2012 [9] D. Cheng and H. Qi, "Controllability and observability of Boolean

    E-Print Network [OSTI]

    Vermont, University of

    . 3, pp. 512521, Mar. 2009. [31] D. Cheng, H. Qi, and Z. Li, Analysis and Control of Boolean Networks: A Semi-Tensor Product Approach. London, U.K.: Springer-Verlag, 2011. [32] D. Cheng, H. Qi, and Z. Li846 IEEE TRANSACTIONS ON NEURAL NETWORKS ON LEARNING SYSTEMS, VOL. 23, NO. 5, MAY 2012 [9] D. Cheng

  17. Combined expert system/neural networks method for process fault diagnosis

    DOE Patents [OSTI]

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

    1995-08-15T23:59:59.000Z

    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.

  18. Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices

    E-Print Network [OSTI]

    Kulkarni, Siddhivinayak

    2009-01-01T23:59:59.000Z

    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...

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

    SciTech Connect (OSTI)

    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

    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.

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

    SciTech Connect (OSTI)

    Johnson, M.L.

    1998-07-01T23:59:59.000Z

    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.