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Optimizing blind source separation with guided genetic algorithms

Górriz , J. M. and Puntonet, Carlos G. and Rojas, F. and Martin, R. and Hornillo, S. and Lang, Elmar (2006) Optimizing blind source separation with guided genetic algorithms. Neurocomputing 69 (13-15), pp. 1442-1457.

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Abstract

This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its application to the problem of blind source separation (BSS) on post-nonlinear mixtures. The paper also includes a formal proof on the convergence of the proposed algorithm using guiding operators, a new concept in the GA ...

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Item Type:Article
Date:2006
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
ValueType
10.1016/j.neucom.2005.12.030DOI
Keywords:Independent component analysis (ICA); Genetic algorithm (GA); Guiding genetic algorithm (GGA); Higher order statistics (HOS); Mutual information
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Owner: Gertraud Kellers
Deposited On:05 Oct 2010 06:32
Last Modified:05 Oct 2010 06:32
Item ID:16914
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