Neural independent component analysis by 'maximum-mismatch' learning principle

Authors:
Simone Fiori
Affiliations:
Faculty of Engineering, University of Perugia, Loc. Pentima bassa, 21, 1-05100 Terni, Italy
Venue:
Neural Networks
Year:
2003

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Abstract

The aim of the present paper is to apply Sudjanto-Hassoun theory of Hebbian learning to neural independent component analysis. The basic learning theory is first recalled and expanded in order to make it suitable for a network of non-linear complex-weighted neurons; then its interpretation and application is shown in the context of blind separation of complex-valued sources. Numerical results are given in order to assess the effectiveness of the proposed learning theory and the related separation algorithm on telecommunication signals; a comparison with other existing techniques finally helps assessing the performances and computational requirements of the proposed algorithm.