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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yang, Z., Wan, Y. (2006). Non-linear Blind Source Separation Using Constrained Genetic Algorithm. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Control and Automation. Lecture Notes in Control and Information Sciences, vol 344. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37256-1_10
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DOI: https://doi.org/10.1007/978-3-540-37256-1_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37255-4
Online ISBN: 978-3-540-37256-1
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