Principal Component Neural Networks

Theory and Applications
1. Auflage 1996
ISBN: 978-0-471-05436-8
Verlag: Wiley

Buch, Englisch, 272 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 572 g

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.

Autoren/Hrsg.

Diamantaras, K I

Kung, S Y

Fachgebiete

• Technische Wissenschaften Technik Allgemein Technik: Allgemeines
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme

Inhaltsverzeichnis

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.

Über Autor(innen)

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.