Principal Component Neural Networks: Theory and Applications / Edition 1

Principal Component Neural Networks: Theory and Applications / Edition 1

by K. I. Diamantaras, S. Y. Kung
     
 

Principal Component Neural Networks Theory and Applications

Understanding the underlying principles of biological perceptual systems is of vital importance not only to neuroscientists, but, increasingly, to engineers and computer scientists who wish to develop artificial perceptual systems. In this original and groundbreaking work, the authors systematically

See more details below

Overview

Principal Component Neural Networks Theory and Applications

Understanding the underlying principles of biological perceptual systems is of vital importance not only to neuroscientists, but, increasingly, to engineers and computer scientists who wish to develop artificial perceptual systems. In this original and groundbreaking work, the authors systematically examine the relationship between the powerful technique of Principal Component Analysis (PCA) and neural networks. Principal Component Neural Networks focuses on issues pertaining to both neural network models (i.e., network structures and algorithms) and theoretical extensions of PCA. In addition, it provides basic review material in mathematics and neurobiology. This book presents neural models originating from both the Hebbian learning rule and least squares learning rules, such as back-propagation. Its ultimate objective is to provide a synergistic exploration of the mathematical, algorithmic, application, and architectural aspects of principal component neural networks. Especially valuable to researchers and advanced students in neural network theory and signal processing, this book offers application examples from a variety of areas, including high-resolution spectral estimation, system identification, image compression, and pattern recognition.

Read More

Product Details

ISBN-13:
9780471054368
Publisher:
Wiley
Publication date:
03/08/1996
Series:
Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control Series, #4
Pages:
272
Product dimensions:
6.34(w) x 9.49(h) x 0.79(d)

Related Subjects

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.

Customer Reviews

Average Review:

Write a Review

and post it to your social network

     

Most Helpful Customer Reviews

See all customer reviews >