Blind source separation of post-nonlinear mixtures using evolutionary computation and order statistics

Authors:
Leonardo Tomazeli Duarte;Ricardo Suyama;Romis Ribeiro de Faissol Attux;Fernando José Von Zuben;João Marcos Travassos Romano
Affiliations:
DSPCOM/LBiC, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, SP, Brazil;DSPCOM/LBiC, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, SP, Brazil;DSPCOM/LBiC, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, SP, Brazil;DSPCOM/LBiC, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, SP, Brazil;DSPCOM/LBiC, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, SP, Brazil
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

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Abstract

In this work, we address the problem of source separation of post-nonlinear mixtures based on mutual information minimization. There are two main problems related to the training of separating systems in this case: the requirement of entropy estimation and the risk of local convergence. In order to overcome both difficulties, we propose a training paradigm based on entropy estimation through order statistics and on an evolutionary-based learning algorithm. Simulation results indicate the validity of the novel approach.