Complex independent component analysis by nonlinear generalized Hebbian learning with Rayleigh nonlinearity

Author

Pomponi, Eraldo ; Fiori, Simone ; Piazza, Francesco

Author_Institution

Dipt. di Elettronica e Autom., Ancona Univ., Italy

Volume

2

fYear

1999

fDate

15-19 Mar 1999

Firstpage

1077

Abstract

This paper presents a non-linear extension of the Sanger´s (1989) generalized Hebbian algorithm to the processing of complex-valued data. A possible choice of the involved nonlinearity is discussed recalling the Sudjianto-Hassoun (1994) interpretation of the nonlinear Hebbian learning. Extension of this interpretation to the complex case leads to a nonlinearity called the Rayleigh function, which allows for separating mixed independent complex-valued source signals

Keywords

Hebbian learning; functional analysis; signal processing; statistical analysis; Rayleigh function; Rayleigh nonlinearity; complex independent component analysis; complex-valued data processing; generalized Hebbian algorithm; mixed independent complex-valued source signals; neural networks; nonlinear generalized Hebbian learning; source signal separation; statistically independent signals; Electronics packaging; Hebbian theory; Independent component analysis; Lagrangian functions; Linearity; Neural networks; Neurons; Principal component analysis; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on

Conference_Location

Phoenix, AZ

ISSN

1520-6149

Print_ISBN

0-7803-5041-3

Type

conf

DOI

10.1109/ICASSP.1999.759930

Filename

759930