On-line Algorithm for Blind Signal Extraction of Arbitrarily Distributed, but Temporally Correlated Sources Using Second Order Statistics

Andrzej Cichocki , Ruck Thawonmas

Abstract

Most of the algorithms for blind separation/extraction and independent component analysis (ICA) can not separate mixtures of sources with extremely low kurtosis or colored Gaussian sources. Moreover, to separate mixtures of super- and sub-Gaussian signals, it is necessary to use adaptive (time-variable) or switching nonlinearities which are controlled via computationally intensive measures, such as estimation of the sign of kurtosis of extracted signals. In this paper, we develop a very simple neural network model and an efficient on-line adaptive algorithm that sequentially extract temporally correlated sources with arbitrary distributions, including colored Gaussian sources and sources with extremely low values (or even zero) of kurtosis. The validity and performance of the algorithm have been confirmed by extensive computer simulation experiments.
Author Andrzej Cichocki (FoEE / ITEEMIS)
Andrzej Cichocki,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
, Ruck Thawonmas
Ruck Thawonmas,,
-
Journal seriesNeural Processing Letters, ISSN 1370-4621, 1573-773X
Issue year2000
Vol12
No1
Pages91-98
Keywords in Englishadaptive learning algorithms, Artificial Intelligence (incl. Robotics), blind signal processing, Electronic and Computer Engineering, neural networks, Nonlinear Dynamics, Complex Systems, Chaos, Neural Networks, Operation Research/Decision Theory
DOIDOI:10.1023/A:1009616029367
URL http://link.springer.com/article/10.1023/A%3A1009616029367
Score (nominal)0
Citation count*66 (2014-02-08)
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