Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/103511
Type: Artigo de evento
Title: Blind Source Separation Of Post-nonlinear Mixtures Using Evolutionary Computation And Order Statistics
Author: Duarte L.T.
Suyama R.
Attux R.R.D.F.
Von Zuben F.J.
Romano J.M.T.
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. © Springer-Verlag Berlin Heidelberg 2006.
Editor: 
Rights: fechado
Identifier DOI: 10.1007/11679363_9
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-33745725572&partnerID=40&md5=a69a143818feb9ab46b8af5fd0582c75
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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