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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/742

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contributor.authorAcharya, D P-
contributor.authorPanda, G-
contributor.authorMishra, S-
contributor.authorLakshmi, Y V S-
identifier.citationInternational Conference on Conference on Computational Intelligence and Multimedia Applications, 2007, 13-15 Dec. 2007 Sivakasi, Tamil Nadu, P 527 - 531en
descriptionCopyright for the paper belongs IEEEen
description.abstractThe present paper proposes a bacteria foraging optimization based independent component analysis (BFOICA) algorithm assuming a linear noise free model. It is observed that the proposed BFOICA algorithm overcomes the long standing permutation ambiguity and recovers the independent components(IC) in a fixed order which depends on the statistical characteristics of the signals to be estimated. The paper compares the performance of BFOICA algorithm with the constrained genetic algorithm based ICA (CGAICA) and most popular fast ICA algorithm. The proposed algorithm offers comparable or even better performance compared to fast ICA algorithm and faster convergence and better mean square error performance compared to CGAICA.en
format.extent331743 bytes-
subjectgenetic algorithmsen
subjectindependent component analysisen
subjectmean square error methodsen
subjectsignal processingen
titleBacteria Foraging Based Independent Component Analysisen
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