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EXECUTION OF A REMOTE-SENSING APPLICATION ON A CUSTOM NEUROCOMPUTER

IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第6期6卷 1505-1515页

作者： WATKINS, SS CHAU, PM TAWEL, R LAMBRIGTSEN, B CALTECH JET PROP LABPASADENACA 91109

A radial basis function neural network was successfully applied to an area which is relatively new for neural networks: a remote sensing application that provides estimates of water vapor content, an important parameter for climate modeling. The neural network provided results which are up to 32% better than had been previously obtained using conventional statistical methods on the same data These results have implications for improved short-term weather forecasting and for long-term global climate modeling. The neural network approach is compared with the past and present operating algorithms at the National Oceanic and Atmospheric Administration, The radial basis function network's performance is compared with sigmoidal backpropagation network. Low-power electronic implementations of the neural methodology were explored to demonstrate the feasibility of placing the network on a remote sensing platform, This would permit processing the ram sensor data into information on the platform, eliminating the need to store the raw data, and helping to contain the expected explosion of climate data.

关键词： Remote sensing Neural networks Radial basis function networks Statistical analysis Weather forecasting Atmospheric modeling Predictive models backpropagation algorithms Low power electronics Explosions

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NEURAL-NETWORK CONTROL FOR DC MOTOR MICROMANEUVERING

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 1995年第5期42卷 516-523页

作者： TZES, A PENG, PY HOUNG, CC Mechanical Engineering Department Polytechnic University Brooklyn NY USA

The application of a neural network controller for compensating the effects induced by the friction in a dc motor micromaneuvering system is considered in this article. A backpropagation neural network operating in the specialized learning mode, using the sign gradient descent algorithm, is employed. The input vector to the neural network controller consists of the time history of the motor angular shaft velocity within a prespecified time window. The on-line training of the neural network is performed in the region of interest of the output domain, The neural network output resembles that of a Pulse Width Modulated controller. The effect of the number of neurons in the input and hidden layers on the transient system response is explored. Experimental studies are presented to indicate the effectiveness of the proposed algorithm.

关键词： Neural networks DC motors Pulse width modulation Control systems Friction backpropagation algorithms Angular velocity control History Shafts Space vector pulse width modulation

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GRADIENT CALCULATIONS FOR DYNAMIC RECURRENT NEURAL NETWORKS - A SURVEY

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第5期6卷 1212-1228页

作者： PEARLMUTTER, BA Learning Systems Department Siemens AG Corporate Research and Development Princeton NJ USA

We survey learning algorithms for recurrent neural networks with hidden units and put the various techniques into a common framework, We discuss fixed point learning algorithms, namely recurrent backpropagation and deterministic Boltzmann machines, and nonfixed point algorithms, namely backpropagation through time, Elman's history cutoff, and Jordan's output feedback architecture, Forward propagation, an on-line technique that uses adjoint equations, and variations thereof, are also discussed, In many cases, the unified presentation leads to generalizations of various sorts, We discuss advantages and disadvantages of temporally continuous neural networks in contrast to clocked ones continue with some ''tricks of the trade'' for training, using, and simulating continuous time and recurrent neural networks, We present some simulations, and at the end, address issues of computational complexity and learning speed.

关键词： Recurrent neural networks backpropagation algorithms Computational modeling Machine learning History Output feedback Equations Neural networks Clocks Computational complexity

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A NEURAL-NETWORK PREESTIMATION FILTER FOR BAD-DATA DETECTION AND IDENTIFICATION IN POWER-SYSTEM STATE ESTIMATION

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ELECTRIC POWER SYSTEMS RESEARCH 1995年第2期34卷 127-134页

作者： SALEHFAR, H ZHAO, R Department of Electrical and Computer Engineering Clarkson University Potsdam NY 13699-5720 USA

Popular state estimation techniques in industry are mostly based on the weighted least squares (WLS) method and its derivatives. These estimators usually detect and identify multiple gross measurement errors by repeating a cycle of estimation-detection-elimination. It is rather time consuming for large systems. This paper presents a neural network preestimation filter to identify most forms of gross errors, including conforming bad data, in raw measurements before state estimation rather than afterwards. The proposed neural network model is trained to be a measurement estimator by using the correct measurements of typical system operating states. Once trained, the filter quickly identifies most forms of gross measurement errors simultaneously by comparing the square difference of the raw measurements and their corresponding estimated values with some given thresholds. System observability is maintained by replacing bad data with their reasonably accurate estimates. Using the proposed neural network preestimation filter, the efficiency of present state estimators is greatly improved. Results from several case studies are presented.

关键词： STATE ESTIMATION DATA PROCESSING NEURAL NETWORKS backpropagation algorithms

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GRADIENT DESCENT LEARNING ALGORITHM OVERVIEW - A GENERAL DYNAMICAL-SYSTEMS PERSPECTIVE

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第1期6卷 182-195页

作者： BALDI, P CALTECH DIV BIOLPASADENACA 91109

We give a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems, This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different model (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. We then briefly examine some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, we focus on the problem of trajectory learning.

关键词： backpropagation algorithms Neurons Context modeling Biological neural networks Calculus Biological systems Organisms Joining processes Hebbian theory Propulsion

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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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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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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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LEARNING WITHOUT LOCAL MINIMA IN RADIAL BASIS FUNCTION NETWORKS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第3期6卷 749-756页

作者： BIANCHINI, M FRASCONI, P GORI, M Dipartimento di Sistemi e Informatica Università di Firenze Italy

Learning from examples plays a central role in artificial neural networks. The success of many learning schemes is not guaranteed, however, since algorithms like backpropagation may get stuck in local minima, thus providing suboptimal solutions. For feedforward networks, the theoretical results reported in [5], [6], [15], and [20] show that optimal learning can be achieved provided that certain conditions on the network and the learning environment are met. A similar investigation is put forward in this paper for the case of networks using radial basis functions (RBF) [10], [14]. The analysis proposed in [6] is extended naturally under the assumption that the patterns of the learning environment are separable by hyperspheres. In that case, we prove that the attached cost function is local minima free with respect to all the weights. This provides us with some theoretical foundations for a massive application of RBF in pattern recognition.

关键词： Intelligent networks Radial basis function networks Pattern recognition Cost function Neural networks Pattern analysis Rough surfaces Surface roughness backpropagation algorithms Artificial neural networks

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USE OF A QUASI-NEWTON METHOD IN A FEEDFORWARD NEURAL-NETWORK CONSTRUCTION ALGORITHM

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第1期6卷 273-277页

作者： SETIONO, R HUI, LCK Department of Information Systems and ComputerScience National University of Singapore Singapore

Interest in algorithms which dynamically construct neural networks has been growing in recent years. This paper describes an algorithm for constructing a single hidden layer feedforward neural network. A distinguishing feature of this algorithm is that it uses the quasi-Newton method to minimize the sequence of error functions associated with the growing network. Experimental results indicate that the algorithm if very efficient and robust. The algorithm was tested on two test problems. The first was the n-bit parity problem and the second was the breast cancer diagnosis problem from the University of Wisconsin Hospitals. For the n-bit parity problem, the algorithm was able to construct neural network having less than n hidden units that solved the problem for n = 4,...,7. For the cancer diagnosis problem, the neural networks constructed by the algorithm had small number of hidden units and high accuracy rates on both the training data and the testing data.

关键词： Intelligent networks Neural networks Feedforward neural networks Testing Heuristic algorithms Robustness Breast cancer Hospitals backpropagation algorithms Training data

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