Neural independent component analysis by 'maximum-mismatch' learning principle.
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.
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013685:Telecommunications