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THEORETICAL INVESTIGATION OF THE ROBUSTNESS OF MULTILAYER PERCEPTRONS - ANALYSIS OF THE LINEAR CASE AND EXTENSION TO NONLINEAR NETWORKS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第3期6卷 560-571页

作者： KERLIRZIN, P REFREGIER, P THOMSON CSF CENT RECH LABOPT GRPF-91404 ORSAYFRANCE

In this paper, we address the problem of robustness in multilayer perceptrons. We present the main theoretical results in the case of linear neural networks with one hidden layer in order to go beyond the empirical study we previously made. We show that the robustness can greatly be improved and that even without decreasing performance in normal use. Finally, we show how this behavior, clearly demonstrated in the linear case, is an approximation of the behavior of nonlinear networks.

关键词： Robustness Multilayer perceptrons Computer aided software engineering Neural networks Degradation Image coding Nonhomogeneous media Performance loss Constraint optimization backpropagation algorithms

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On stochastic approximation algorithms for classes of PAC learning problems

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1997年第3期27卷 516-522页

作者： Rao, NSV Uppuluri, VRR Oblow, EM Center for Engineering SystemsAdvanced Research Oak Ridge National Laboratory Oak Ridge TN USA

The classical stochastic approximation methods are shown to yield algorithms to solve several formulations of the PAC learning problem defined on the domain [0,1](d). Under some smoothness conditions on the probability measure functions, simple algorithms to solve some PAC learning problems are proposed based on networks of nonpolynomial units (e.g. artificial neural networks). Conditions on the sizes of the samples required to ensure the error bounds are derived using martingale inequalities.

关键词： Stochastic processes Approximation algorithms backpropagation algorithms Artificial neural networks Neural networks Approximation methods Risk management Pattern recognition Concrete Power engineering and energy

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APPLICATION OF A MAXIMUM-LIKELIHOOD ESTIMATOR IN AN EXPERIMENTAL-STUDY IN ULTRASONIC DIFFRACTION TOMOGRAPHY

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1993年第3期12卷 545-554页

作者： TSIHRINTZIS, GA DEVANEY, AJ NORTHEASTERN UNIV DEPT ELECT & COMP ENGNBOSTONMA 02115 AJ DEVANEY ASSOCIATES INC BOSTONMA 02116

We evaluate a recently proposed approach [1]-[3] to the inverse problem of detection of the presence and estimation of the location of a known object from data collected in a set of diffraction tomographic experiments. We verify on experimental data the validity of the filtered backpropagation algorithms developed in [1]-[3], demonstrate their robustness to modeling errors and to severe limitations in the angular coverage of the tomographic data, and illustrate a potential application in medical imaging of soft tissue.

关键词： Maximum likelihood estimation Tomography Maximum likelihood detection Inverse problems Object detection Diffraction backpropagation algorithms Robustness Biomedical imaging Biological tissues

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PERFORMANCE EVALUATION OF MULTILAYER PERCEPTRONS IN SIGNAL-DETECTION AND CLASSIFICATION

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第2期6卷 381-386页

作者： MICHALOPOULOU, ZH NOLTE, LW ALEXANDROU, D DUKE UNIV DEPT ELECT ENGNDURHAMNC 27706

Multilayer perceptrons trained with the backpropagation algorithm are tested in detection and classification tasks and are compared to optimal algorithms resulting from likelihood ratio tests. The focus is on the problem of one of M orthogonal signals in a Gaussian noise environment, since both the Bayesian detector and classifier are known for this problem and can provide a measure for the performance evaluation of the neural networks. Two basic situations are considered: detection and classification. For the detection part, it was observed that for the signal-known-exactly case (M = 1) the performance of the neural detector converges to the performance of the ideal Bayesian decision processor, while for a higher degree of uncertainty (i.e., for a larger M) the performance of the multilayer perceptron is inferior to that of the optimal detector. For the classification case the probability of error of the neural network is comparable to the minimum Bayesian error, which can be numerically calculated. Adding noise during the training stage of the network does not affect the performance of the neural detector;however, there is an indication that the presence of noise in the learning process of the neural classifier results in a degraded classification performance.

关键词： Multilayer perceptrons Detectors Bayesian methods backpropagation algorithms Testing Neural networks Gaussian noise Noise measurement Signal processing Uncertainty

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On-line adaptive control of non-linear plants using neural networks with application to liquid level control

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 1998年第1期12卷 13-28页

作者： Ng, GW Cook, PA Univ Manchester Inst Sci & Technol Dept EEE Control Syst Ctr Manchester M60 1QD Lancs England

In this paper, we present two on-line adaptive control algorithms for non-linear plants using neural networks. The architecture used is based on the concept of specialized learning, which was first proposed by Psaltis et al.(4) and suffers from two main problems, namely lack of knowledge of the plant Jacobian and slow training speed if the standard backpropagation algorithm is used. Specialized learning has been tested successfully by several researchers using the sign of the plant Jacobian, chosen on the basis of prior qualitative knowledge of the plant. It has also been proposed to calculate the plant Jacobian through a pretrained model. However, if off-line training of the model is not possible and qualitative knowledge of the plant Jacobian is not available, then specialised learning may not be feasible for an on-line neurocontroller. We propose that it is possible to estimate the plant Jacobian through the model on-line for a slowly varying plant. Some analysis for calculating the plant Jacobian on-line is discussed. For rapidly varying plant, we propose using the recursive least-squares (RLS) training algorithm instead of the standard back-propagation (BP) algorithm in the specialised learning architecture. We show that RLS training is faster than the BP algorithm. The proposed fast algorithm is then tested on simulated non-linear plants as well as in a real-time application to a coupled-tanks test rig. (C) 1998 John Whey & Sons, Ltd.

关键词： Distress network applications Neural network adaptive control backpropagation algorithms Liquid level control knowledge IN-LINE level control Jacobian

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AN ARTIFICIAL NEURAL NETWORK FOR SPECT IMAGE-RECONSTRUCTION

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IEEE TRANSACTIONS ON MEDICAL IMAGING 1991年第3期10卷 485-487页

作者： FLOYD, CE DUKE UNIV DEPT BIOMED ENGNDURHAMNC 27710

An artificial neural network has been developed to reconstruct quantitative single photon emission computed tomographic (SPECT) images. The network is trained with an ideal projection-image pair to learn a shift-invariant weighting (filter) for the projections. Once trained, the network produces weighted projections as a hidden layer when acquired projection data are presented to its input. This hidden layer is then backprojected to form an image as the network output. The learning algorithm adjusts the weighting coefficients using an algorithm (backpropagation) which minimizes the mean squared error between the ideal training image and the reconstructed training image. In the language of neural networks, this is a two-layer network with one hiden layer (the weighted projection) whose weights are trained using backpropagation with supervised training and a modified delta rule. The response of the trained network to an impulse projection resembles the ramp filter typically used with backprojection and reconstructed images are similar to filtered backprojection images. This encouraging result indicates that this artificial neural network architecture may be useful for investigating the tomographic reconstruction process.

关键词： Photon emission learning algorithms backpropagation algorithms single photon emission computed tomography image reconstruction Weighting coefficients weighting filter Image backprojection

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APPLICATION OF NEURAL NETWORKS TO SIGNAL PREDICTION IN NUCLEAR-POWER-PLANT

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 1993年第5期40卷 1337-1341页

作者： KIM, WJ CHANG, SH LEE, BH Department of Nuclear Engineering Korea Advanced Institute of Science and Technology Taejon South Korea

This paper describes the feasibility study of an artificial neural network for signal prediction. The purpose of signal prediction is to estimate the value of undetected next time step signal. As the prediction method, based on the idea of auto regression, a few previous signals are inputs to the artificial neural network and the signal value of next time step is estimated with the outputs of the network. The artificial neural network can be applied to the nonlinear system and answers in short time. The training algorithm is a modified backpropagation model, which can effectively reduce the training time. The target signal of the simulation is the steam generator water level, which is one of the important parameters in nuclear power plants. The simulation result shows that the predicted value follows the real trend well.

关键词： Neural networks Artificial neural networks Power generation Prediction methods Nonlinear systems backpropagation algorithms Power system modeling Nuclear power generation Signal generators Predictive models

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TRAINING FEEDFORWARD NETWORKS WITH THE MARQUARDT ALGORITHM

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1994年第6期5卷 989-993页

作者： HAGAN, MT MENHAJ, MB

The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the-other techniques when the network contains no more than a few hundred weights.

关键词： feedforward network function approximation algorithms backpropagation algorithms feedforward neural nets Nonlinear least squares conjugate gradient learning rate

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A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1999年第1期29卷 62-72页

作者： Chen, CLP Wan, JZ Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA USAF Wright Lab Mat Directorate MLIM Wright Patterson AFB OH 45433 USA Lexis Nexis Data Cent Dayton OH 45343 USA

A fast learning algorithm is proposed to find an optimal weights of the flat neural networks (especially, the functional-link network). Although the hat networks are used for nonlinear function approximation, they can be formulated as linear systems. Thus, the weights of the networks can be solved easily using a linear least-square method. This formulation makes it easier to update the weights instantly for both a new added pattern and a new added enhancement node. A dynamic stepwise updating algorithm is proposed to update the weights of the system on-the-fly. The model is tested on several time-series data including an infrared laser data set, a chaotic time-series, a monthly flour price data set, and a nonlinear system identification problem. The simulation results are compared to existing models in which more complex architectures and more costly training are needed. The results indicate that the proposed model is very attractive to real-time processes.

关键词： Heuristic algorithms Neural networks Radial basis function networks backpropagation algorithms Function approximation Linear systems Laser modes Chaos Nonlinear systems Equations

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TONE RECOGNITION OF ISOLATED CANTONESE SYLLABLES

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1995年第3期3卷 204-209页

作者： LEE, T CHING, PC CHAN, LW CHENG, YH MAK, B The Department of Electronic Engineering Chinese University of Hong Kong Sha Tin Hong Kong China Department of Computer science Chinese University of Hong Kong Hong Kong China Motorola Semiconductors Hong Kong Limited Hong Kong China

Tone identification is essential for the recognition of Chinese language, specifically for Cantonese which is well known of being very rich in tones. This paper presents an efficient method for tone recognition of isolated Cantonese syllables, Suprasegmental feature parameters are extracted from the voiced portion of a monosyllabic: utterance and a three-layer feedforward neural network is used to classify these feature vectors. Using a phonologically complete vocabulary of 234 distinct syllables, the recognition accuracy for single-speaker and multispeaker is given by 89.0% and 87.6% respectively.

关键词： Natural languages Neural networks Feature extraction Feedforward neural networks Speech recognition Multi-layer neural network Vocabulary backpropagation algorithms Neurons Computer science

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