Neural independent component analysis by "maximum-mismatch" learning principle
نویسنده
چکیده
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.
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ورودعنوان ژورنال:
- Neural networks : the official journal of the International Neural Network Society
دوره 16 8 شماره
صفحات -
تاریخ انتشار 2003