Actas de congresos
Blind Source Separation Of Post-nonlinear Mixtures Using Evolutionary Computation And Order Statistics
Registro en:
3540326308; 9783540326304
Lecture Notes In Computer Science (including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). , v. 3889 LNCS, n. , p. 66 - 73, 2006.
3029743
10.1007/11679363_9
2-s2.0-33745725572
Autor
Duarte L.T.
Suyama R.
Attux R.R.D.F.
Von Zuben F.J.
Romano J.M.T.
Institución
Resumen
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. 3889 LNCS
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