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Non-linear Blind Source Separation Using Constrained Genetic Algorithm

  • Zuyuan Yang
  • Yongle Wan
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 344)

Abstract

In this paper, a novel adaptive algorithm based on constrained genetic algorithm (GA) is presented for solving non-linear blind source separation (BSS), which can both get out of the trap of local minima and restrict the stochastic decision of GA. The approach utilizes odd polynomials to approximate the inverse of non-linear mixing functions and encodes the separating matrix and the coefficients of the polynomials simultaneously. A novel objective function based on mutual information is used with the constraints to the separating matrix and the coefficients of the polynomials respectively. The experimental results demonstrate the feasibility, robustness and parallel superiority of the proposed method.

Keywords

Genetic Algorithm Mutual Information Blind Source Separation Blind Signal Maximum Iteration Number 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zuyuan Yang
    • 1
  • Yongle Wan
    • 1
  1. 1.School of Electrics & Information EngineeringSouth China University of TechnologyGuangzhou, GuangdongChina

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