Blind Source Separation of Post-Nonlinear Mixtures Using Evolutionary Computation and Gaussianization

被引：0
|
作者：
Dias, Tiago M. [1 ,3 ]
Attux, Romis [2 ,3 ]
Romano, Joao M. T. [3 ]
Suyama, Ricardo [3 ]
机构：
[1] Univ Estadual Campinas, Dept Microwave & Opt, CP 6101, BR-13083970 Campinas, SP, Brazil
[2] Univ Estadual Campinas, Dept Comp Engn, Dipartimento Automaz Ind, Campinas, SP, Brazil
[3] Univ Estadual Campinas, Sch Elect & Comp Engn, DSPCOM Lab Signal Proc Commun, Campinas, SP, Brazil
来源：
INDEPENDENT COMPONENT ANALYSIS AND SIGNAL SEPARATION, PROCEEDINGS | 2009年 / 5441卷
关键词：
Nonlinear blind source separation; post-nonlinear models; gaussianization; evolutionary algorithms; artificial immune systems;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this work, we propose a new method for source separation of post-nonlinear mixtures that combines evolutionary-based global search, gaussianization and a local search step based on FastICA algorithm. The rationale of the proposal is to attempt to obtain efficient and precise solutions using with parsimony the available computational resources, and, as shown by the simulation results, this aim was satisfactorily fulfilled.
引用
收藏
页码：235 / +
页数：2
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