Neural Processing Letters

, Volume 12, Issue 1, pp 91–98 | Cite as

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

  • Andrzej Cichocki
  • Ruck Thawonmas
Article

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.

adaptive learning algorithms blind signal processing neural networks 

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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Andrzej Cichocki
    • 1
    • 2
  • Ruck Thawonmas
    • 3
  1. 1.Laboratory for Open Information SystemsBrain Science Institute, RIKENSaitamaJapan
  2. 2.Warsaw University of TechnologyWarsawPoland
  3. 3.Department of Information Systems EngineeringKochi University of TechnologyKochiJapan

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