A general class of ψ-APEX PCA neural algorithms

Author

Fiori, Simone ; Piazza, Francesco

Author_Institution

Neural Networks & Adaptive Syst. Res. Group, Perugia Univ., Italy

Volume

47

Issue

9

fYear

2000

fDate

9/1/2000 12:00:00 AM

Firstpage

1394

Lastpage

1397

Abstract

Principal component analysis (PCA) can be successfully applied to a variety of signal processing problems. Different analyzers have been reported in the scientific literature; among others, the Adaptive Principal component EXtractor (APEX) by Kung and Diamantaras has attracted much interest in the scientific community since it involves a specific neural architecture and a specific learning theory. The aim of this brief is to present a general class of APEX-like learning rules (referred to as ψ-APEX) and to illustrate their features by theoretical and numerical analysis.

Keywords

adaptive signal processing; learning (artificial intelligence); neural net architecture; principal component analysis; ψ-APEX PCA neural algorithm; adaptive principal component extraction; hierarchical neural network architecture; learning rules; principal component analysis; signal processing; Circuits; Convergence; Equations; Iterative algorithms; Neural networks; Principal component analysis; Recurrent neural networks; Unsupervised learning;

fLanguage

English

Journal_Title

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on

Publisher

ieee

ISSN

1057-7122

Type

jour

DOI

10.1109/81.883336

Filename

883336