Description
Book SynopsisSystematically 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 ContentsA 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.
