Principal Component Neural Networks: Theory and Applications

by Konstantinos Diamantaras

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.

272 pages, Hardcover

First published February 1, 1996

About the author

Konstantinos I. Diamantaras