Please use this identifier to cite or link to this item:
|Type:||Artigo de evento|
|Title:||Blind Source Separation Of Post-nonlinear Mixtures Using Evolutionary Computation And Order Statistics|
Von Zuben F.J.
|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.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.