Principal component neural networks : theory and applications
Author(s)
Bibliographic Information
Principal component neural networks : theory and applications
（Adaptive and learning systems for signal processing, communications, and control）
Wiley, c1996
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Note
"A Wiley-Interscience publication."
Includes bibliographical references (p. 241-247) and index
Description and Table of Contents
Description
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.
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.
by "Nielsen BookData"