An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation

Author

Cheung, Yiu-Ming ; Xu, Lei

Author_Institution

Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong

Volume

2

fYear

1999

fDate

36342

Firstpage

955

Abstract

This paper presents a novel approach, called adaptive polynomial power learning estimation (APPLE) based ICA algorithm, for independent component analysis (ICA) problem. In this algorithm, the form of separation nonlinearity is fixed at polynomial function, but the exponent is adaptive adjusted in implementation. Experiments have demonstrated that this algorithm can successfully separate the combinations of sub-Gaussian and super-Gaussian signals

Keywords

adaptive signal processing; information theory; learning (artificial intelligence); neural nets; principal component analysis; Gaussian signals; adaptive polynomial power learning estimation; adaptive-learned polynomial nonlinearity; independent component analysis; information theory; neural nets; probability; signal separation; Computer science; Independent component analysis; Neural networks; Partial response channels; Polynomials; Power engineering and energy; Signal analysis; Source separation; Speech recognition; Wireless communication;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.831082

Filename

831082