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Optimizing blind source separation with guided genetic algorithms

Górriz, J. M., Puntonet, Carlos G., Rojas, F., Martin, R., 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
Dewey Decimal Classification:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Item ID:16914
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