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
     
 

ISBN-10: 0471054364

ISBN-13: 9780471054368

Pub. Date: 03/08/1996

Publisher: Wiley

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

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Overview

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)

Related Subjects

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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