Neural Networks for Optimization and Signal Processing

by Andrzej Cichocki, R. Unbehauen

Overview

A topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer simulation results.

Product Details

ISBN-13:	9780471930105
Publisher:	Wiley
Publication date:	06/07/1993
Pages:	544
Product dimensions:	6.24(w) x 9.21(h) x 1.50(d)

About the Author

Andrzej Cichocki received the M.Sc. (with honors), Ph.D. and Dr.Sc. (Habilitation) degrees, all in electrical engineering, from Warsaw University of Technology in Poland.

Since 1972, he has been with the Institute of Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering at the Warsaw University of Technology, where he obtain a title of a full Professor in 1995.

He spent several years at University Erlangen-Nuerenberg in Germany, at the Chair of Applied and Theoretical Electrical Engineering directed by Professor Rolf Unbehauen, as an Alexander-von-Humboldt Research Fellow and Guest Professor. In 1995-1997 he was a team leader of the laboratory for Artificial Brain Systems, at Frontier Research Program RIKEN (Japan), in the Brain Information Processing Group.

Table of Contents

Mathematical Preliminaries of Neurocomputing.

Architectures and Electronic Implementation of Neural Network Models.

Unconstrained Optimization and Learning Algorithms.

Neural Networks for Linear, Quadratic Programming and Linear Complementarity Problems.

A Neural Network Approach to the On-Line Solution of a System of Linear Algebraic Equations and Related Problems.

Neural Networks for Matrix Algebra Problems.

Neural Networks for Continuous, Nonlinear, Constrained Optimization Problems.

Neural Networks for Estimation, Identification and Prediction.

Neural Networks for Discrete and Combinatorial Optimization Problems.

Appendices.

Subject Index.

Editorial Reviews

The theory built up around neural networks is at present so extensive and the applications so numerous that no treatment of the subject can be comprehensive. Here the selection and treatment of material reflect the authors' backgrounds as electrical engineers and researchers working in the area of circuit and system theory. The book is not intended to address many of the biological aspects of neural network models or to consider biological behavioral and cognitive phenomena. The focus is on such applications as optimization, signal processing, optimal control, and identification. Math prerequisites include linear algebra, ordinary differential equations, multivariable analysis, and basic probability and statistics. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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