Principal Component Neural Networks: Theory and Applications / Edition 1 available in Hardcover
- Pub. Date:
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.
|Series:||Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control Series , #4|
|Product dimensions:||6.34(w) x 9.49(h) x 0.79(d)|
About the Author
K. I. Diamantaras is a research scientist at Aristotle Universityin Thessaloniki, Greece. He received his PhD from PrincetonUniversity and was formerly a research scientist for SiemansCorporate Research.S. Y. Kung is Professor of Electrical Engineering at PrincetonUniversity and received his PhD from Stanford University. He wasformerly a professor of electrical engineering at the University ofSouthern California.
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.