Principal Component Neural Networks

Theory and Applications

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

Buch | Hardcover

272 Seiten

1996
Wiley-Interscience (Verlag)
9780471054368 (ISBN)

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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.

K. I. Diamantaras is a research scientist at Aristotle University in Thessaloniki, Greece. He received his PhD from Princeton University and was formerly a research scientist for Siemans Corporate Research. S. Y. Kung is Professor of Electrical Engineering at Princeton University and received his PhD from Stanford University. He was formerly a professor of electrical engineering at the University of Southern California.

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.

Erscheint lt. Verlag	4.4.1996
Reihe/Serie	Adaptive and Learning Systems for Systems for Signal Processing, Communications, and Control Series
Sprache	englisch
Maße	161 x 241 mm
Gewicht	567 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Technik ► Elektrotechnik / Energietechnik
ISBN-13	9780471054368 / 9780471054368
Zustand	Neuware