Neural Networks for Optimization and Signal Processing / Edition 1

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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.

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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 (
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Product Details

  • ISBN-13: 9780471930105
  • Publisher: Wiley
  • Publication date: 6/15/1993
  • Edition number: 1
  • Pages: 548
  • Product dimensions: 6.24 (w) x 9.21 (h) x 1.50 (d)

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.


Subject Index.

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