Principal Component Neural Networks
Theory and Applications

K. I. Diamantaras (Author), S. Y. Kung (Author)

9780471054368, Wiley

Hardback, published 4 April 1996

272 pages
24.1 x 16.1 x 2 cm, 0.567 kg

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.

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.

Subject Areas: Electronics & communications engineering [TJ]