ISBN-10:
0471054364
ISBN-13:
9780471054368
Pub. Date:
03/08/1996
Publisher:
Wiley
Principal Component Neural Networks: Theory and Applications / Edition 1

Principal Component Neural Networks: Theory and Applications / Edition 1

by K. I. Diamantaras, S. Y. Kung

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Overview

Principal Component Neural Networks: Theory and Applications / Edition 1

Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas.

Product Details

ISBN-13: 9780471054368
Publisher: Wiley
Publication date: 03/08/1996
Series: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control Series , #4
Pages: 272
Product dimensions: 6.34(w) x 9.49(h) x 0.79(d)

About the Author

K. I. Diamantaras is a research scientist at Aristotle Universityin Thessaloniki, Greece. He received his PhD from PrincetonUniversity and was formerly a research scientist for SiemansCorporate Research.

S. Y. Kung is Professor of Electrical Engineering at PrincetonUniversity and received his PhD from Stanford University. He wasformerly a professor of electrical engineering at the University ofSouthern California.

Table of Contents

A Review of Linear Algebra.

Principal Component Analysis.

PCA Neural Networks.

Channel Noise and Hidden Units.

Heteroassociative Models.

Signal Enhancement Against Noise.

VLSI Implementation.

Appendices.

Bibliography.

Index.

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