Recurrent Neural Networks For Blind Separation of Sources
نویسندگان
چکیده
In this paper, fully connected recurrent neural networks are investigated for blind separation of sources. For these networks, a new class of unsupervised on-line learning algorithms are proposed. These algorithms are the generalization of the Hebbian/anti-Hebbian rule. They are not only biologically plausible but also theoretically sound. An important property of these algorithms is that the performance of the networks is independent of the mixing matrix and the scaling factor of the input sources. This property is veri ed by analyses and simulations.
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