Post-nonlinear mixtures BSS based on an improved genetic algorithm

doi:10.7523/j.issn.2095-6134.2012.5.019

Abstract

Abstract: Considering the deficiency of nonlinear parameter estimation in post-nonlinear mixtures BSS, we propose a method based on an improved GA. First, the value of a new fitness function is used to reflect on the choice of crossover and mutation probabilities. PE and SA are incorporated in GA. Finally, the linear separation algorithm is applied to estimate the separation matrix. The results indicate that the new method has better performance than conventional algorithms.

Key words: genetic algorithm (GA), BSS, post-nonlinear mixtures, simulated nnealing (SA), priority evolution(PE)

CLC Number:

TN911.7

SANG Rui, WU Jie, XU Hua, GUO Qiang. Post-nonlinear mixtures BSS based on an improved genetic algorithm[J]. , 2012, 29(5): 707-713.

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