Extended Hebbian learning for blind separation of complex-valued sources

Author

Fiori, Simone

Author_Institution

Neural Networks & Circuit Theor. Res. Group, Perugia Univ., Terni, Italy

Volume

50

Issue

4

fYear

2003

fDate

4/1/2003 12:00:00 AM

Firstpage

195

Lastpage

202

Abstract

The aim of this work is to present a nonlinear extension to Sanger´s generalized Hebbian learning algorithm for complex-valued signal processing by neural networks. A possible choice of the involved nonlinearity is discussed by recalling the Sudjianto-Hassoun interpretation of nonlinear Hebbian learning. An extension of this interpretation to the complex-valued case leads to a Rayleigh nonlinearity, that allows for separating mixed independent complex-valued circular source signals.

Keywords

Hebbian learning; blind source separation; neural nets; Rayleigh nonlinearity; artificial neural networks; blind source separation; complex-valued signal processing; complex-valued sources; complex-weighted networks; extended Hebbian learning; generalized Hebbian learning algorithm; mixed independent complex-valued circular source signals; nonlinear extension; Artificial neural networks; Biological neural networks; Blind source separation; Circuits; Digital signal processing; Hebbian theory; Principal component analysis; Signal processing; Signal processing algorithms; Source separation;

fLanguage

English

Journal_Title

Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7130

Type

jour

DOI

10.1109/TCSII.2003.810486

Filename

1193576