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Principal Component Neural Networks. Theory and Applications. Edition No. 1. Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control

  • ID: 2172375
  • Book
  • April 1996
  • 272 Pages
  • John Wiley and Sons Ltd
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.
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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.
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K. I. Diamantaras Aristotle University, Thessaloniki, Greece.

S. Y. Kung Princeton University.
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