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Artificial-neural-network-based fast valving control in a power-generation system

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 1997年第2期10卷 139-155页

作者： Han, YD Wang, ZH Chen, Q Tan, SH NATL UNIV SINGAPORE DEPT ELECT ENGNSINGAPORE 119260SINGAPORE TSING HUA UNIV BEIJING 100084PEOPLES R CHINA

This paper presents an artificial-neural-network-based controller to realize fast valving in a power-generation plant A backpropagation. algorithm is used to train the feedforward neural-network controller The hardware implementation and the test results of the controller on a physical pilot-scale power system set-up are described in detail. Compared with some conventional fast valving methods applied to the same system, test results (both in a computer simulation and on a physical pilot-scale power system set-up) show that the neural-network controller has quite satisfactory generalisation capability, feasibility and reliability, as well as accuracy. (C) 1997 Elsevier Science Ltd.

关键词： power-generation systems fast valving transient stability artificial neural networks backpropagation algorithm pilot-scale power systems set-ups hardware implementation

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Fast training of multilayer perceptrons

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1997年第6期8卷 1314-1320页

作者： Verma, B Laboratory of Chromatography DEPg.Fac.Quimica Universidad Nacional Autonoma de Mexico Circuito interior Cd Universitaria/CP 04510 Mexico D.F.Mexico

Training a multilayer perceptron by an error backpropagation algorithm is slow and uncertain, This paper describes a new approach which is much faster and certain than error backpropagation, The proposed approach is based on combined iterative and direct solution methods, In this approach, we use an inverse transformation for linearization of nonlinear output activation functions, direct solution matrix methods for training the weights of the output layer;and gradient descent, the delta rule, and other proposed techniques for training the weights of the hidden layers, The approach has been implemented and tested on many problems, Experimental results, including training times and recognition accuracy, are given, Generally, the approach achieves accuracy as good as or better than perceptrons trained using error backpropagation, and the training process is much faster than the error backpropagation algorithm and also avoids local minima and paralysis.

关键词： artificial neural networks backpropagation algorithm local minima multilayer perceptrons supervised learning

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On fault tolerant training of feedforward neural networks

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NEURAL NETWORKS 1997年第3期10卷 539-553页

作者： Arad, BS ElAmawy, A LOUISIANA STATE UNIV DEPT ELECT & COMP ENGNBATON ROUGELA 70803

This paper presents an extensive study of fault tolerant training of feedforward artificial neural networks. We present several versions of a very robust training algorithm and report the results of their simulations. Our algorithm is shown to outperform all existing training algorithms in its ability to tolerate different fault types and larger number of hidden unit failures. We show that the generalization ability of the proposed algorithm is substantially better than that of the standard backpropagation algorithm and is comparable with that of other existing fault tolerant algorithms. The algorithm is based on the backpropagation algorithm with built-in measures for extensive fault tolerant training. A novel concept presented in this paper is that of training the network for fault types beyond the limits of the activation function. We demonstrate that training for such unrealistic fault types enables the network to be more tolerant to realistic fault types within the limits of the activation function. Further, tradeoffs between training time, enhanced fault tolerance, and generalization properties are studied. (C) 1997 Elsevier Science Ltd.

关键词： artificial neural network fault tolerant training efficiency generalization backpropagation algorithm feedforward network neuron failure fault types

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Necessary Conditions for Multilayer Nets having Solutions and Convergent Superiority of Bipolar Nets

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IFAC Proceedings Volumes 1997年第11期30卷 789-794页

作者： Hiromu Gotanda Hiroshi Shiratsuchi Katsuhiro Inoue Kousuke Kumamaru Faculty of Engineering Kinki University in Kyushu Iizuka Fukuoka 820 Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka 820 Japan

This paper formulates a necessary condition for multilayer nets to have solutions by a set of normal vectors orthogonal to separation hyperplanes. Comparing the necessary condition to the distributions of normal vectors with the weights and biases initialized ordinarily by random numbers with zero mean, it is derived that bipolar nets are superior to unipolar nets in convergence of the back propagation learning initialized in such an ordinary manner.

关键词： Neural nets backpropagation algorithm convergence nonlinear models

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Identification of Plant Growth Under DIF Condition

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IFAC Proceedings Volumes 1997年第11期30卷 1709-1714页

作者： Hiroshi Shimizu Minoru Yamazaki Faculty of Agricultural Engineering Kyoto University Kitashirakawa Oiwake-cho Sakyo-ku Kyoto 606-01 JAPAN

A neural network was used to identify the stem elongation of a plant The network architecture was three layers network, input layer, hidden layer and output layer. The input data were environmental conditions, DIF and photoperiod, for plant growth, and the output data were coefficients of certain equation that represents plant stem elongation. The network was trained using the backpropagation algorithm. An experiment for measuring the stem elongation of a plant was conducted to collect data for verifying the network output.

关键词： Agriculture Identification Neural networks backpropagation algorithm Models

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A SCALABLE PARALLEL FORMULATION OF THE backpropagation algorithm FOR HYPERCUBES AND RELATED ARCHITECTURES

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 1994年第10期5卷 1073-1090页

作者： KUMAR, V SHEKHAR, S AMIN, MB Department of Computer Science University of Minnesota Minneapolis MN USA

In this paper, we present a new technique for mapping the backpropagation algorithm on hypercubes and related architectures. A key component of this technique is a network partitioning scheme called checkerboarding, Checkerboarding allows ns to replace the ail-to-all broadcast operation performed by the commonly used vertical network partitioning scheme, with operations that are much faster on the hypercubes and related architectures. Checkerboarding can be combined with the pattern partitioning technique to form a hybrid scheme that performs better than either one of these schemes. Theoretical analysis and experimental results on nCUBE(R) and CM5(R) show that our scheme performs better than the other schemes, for both uniform and nonuniform networks.

关键词： backpropagation algorithm HYPERCUBE SCALABILITY NEURAL NETWORKS NETWORK PARTITIONING PATTERN PARTITIONING

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PERFORMANCE OF THE GENERALIZED DELTA-RULE IN STRUCTURAL DAMAGE DETECTION

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 1995年第2期8卷 211-221页

作者： BARAI, SV PANDEY, PC INDIAN INST SCI DEPT CIVIL ENGNBANGALORE 560012KARNATAKAINDIA

The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage stares generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and strains) at suitable selected locations in the structure. Issues related to the performance of the network with reference to hidden layers and hidden. neurons are examined. Some heuristics are proposed for the design of neural networks for damage identification in structures. These are further supported by an investigation conducted on five other bridge truss configurations.

关键词： ARTIFICIAL NEURAL NETWORKS (ANN) backpropagation algorithm BRIDGE STRUCTURE DAMAGE DETECTION FEM GENERALIZED DELTA RULE (GDR) MULTILAYER PERCEPTRONS NETWORK ARCHITECTURE STRUCTURAL IDENTIFICATION TESTING PATTERNS TRAINING PATTERNS

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Complex Function Approximation Based on Multi-ANN Approach

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Journal of Systems Engineering and Electronics 1995年第2期6卷 22-31页

作者： Li Renhou & Gao Feng (Institute of Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China) Institute of Systems Engineering Xi'an Jiaotong University Xi'an 710049 China

This paper presents a multi-ANN approximation approach to approximate complex non-linear function. Comparing with single-ANN methods the proposed approach improves and increases the approximation and generalization ability, and adaptability greatly in learning processes of networks. The simulation results have been shown that the method can be applied to the modeling and identification of complex dynamic control systems.

关键词： backpropagation algorithm Multilayer perception Complex function approximation Input space partitioning.

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ARTIFICIAL NEURAL-NETWORK STUDIES IN QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS OF ANTIFUNGAL AZOXY COMPOUNDS

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EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY 1995年第7-8期30卷 569-574页

作者： HASEGAWA, K DEUSHI, T YAEGASHI, O MIYASHITA, Y SASAKI, S TOYOHASHI UNIV TECHNOL DEPT KNOWLEDGE BASED INFORMAT ENGNTOYOHASHIAICHI 441JAPAN KOWA CO LTD TOKYO RES LABSHIGASHIMURAYAMATOKYO 189JAPAN

Artificial neural networks (ANN) based on the back-propagation algorithm (BP algorithm) were applied to a quantitative structure-activity relationship (QSAR) study for 30 azoxy compounds with antifungal activity. The ANN model could well explain the variance of the antifungal activity owing to its ability to deal with a nonlinear tendency in the data set. A modified BP algorithm proposed by the authors has provided the ANN model with a more enhanced predictive capability. Finally a transformation of the final ANN model to a polynomial of original physico-chemical parameters was shown to be useful to elucidate the structural requirements for the antifungal activity.

关键词： ARTIFICIAL NEURAL NETWORK backpropagation algorithm QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP AZOXY COMPOUND ANTIFUNGAL ACTIVITY

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INTERPRETATION OF NONSTRESS TESTS BY AN ARTIFICIAL NEURAL-NETWORK

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AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY 1995年第5期172卷 1372-1379页

作者： KOL, S THALER, I PAZ, N SHMUELI, O RAMBAM MED CTR DEPT OBSTET & GYNECOLIL-31096 HAIFAISRAEL TECHNION ISRAEL INST TECHNOL FAC MEDIL-32000 HAIFAISRAEL TECHNION ISRAEL INST TECHNOL FAC COMP SCIIL-32000 HAIFAISRAEL

OBJECTIVE: Our purpose was to evaluate an artificial neural network in the interpretation of nonstress tests, STUDY DESIGN: A nonlinear artificial neural network trained by backpropagation was taught to interpret records of nonstress tests by two learning sets. The first set contained nonstress tests that were similarly interpreted by three human experts;the second set contained a subset of nonstress tests that led to interobserver disagreement. Both ''raw'' fetal heart rats and uterine contraction data and 17 quantified variables obtained by automated computer analysis were introduced to the input layer. After training, the network was tested by presenting it with input patterns to which it had not been exposed. The performance of the system was examined in relation to the human expert. RESULTS: After training the neural network with the first set, a sensitivity of 88.9% and a false-positive rate of 4.3% were obtained at testing. When the teaming acid test set contained records that led to interobserver disagreement, a sensitivity of 86.7% and a false-positive rate of 19.7% were obtained. Sixty percent of fetal heart rate records interpreted as abnormal by the neural network were interpreted likewise by the human experts. CONCLUSIONS: The results obtained are encouraging in that the neural network could discriminate between normal and abnormal nonstress tests. Further evaluation of this new technique is mandatory to evaluate its efficacy acid reliability in interpreting fetal heart rate records.

关键词： NONSTRESS TEST ARTIFICIAL NEURAL NETWORK backpropagation algorithm AUTOMATED ANALYSIS OF FETAL HEART RATE RECORDS

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