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Neural learning algorithms developed for blind separation of mixed source signals give rise to a Global Separating-Mixing (GSM) matrix that can be used to measure the performance of the unmixing system. In the case of the instantaneous linear noiseless mixing model, we consider the GSM as a transformation operator and show that it is equivalent to a(More)
The non-linearities in the objective functions play an important role in the convergence and stability of various neural ICA algorithms. In case of maximization of non-gaussianity, they influence the negentropy and in Maximum Likelihood Estimation (MLE), they are related to the assumed distributions of sources. We present in this paper an experimental(More)
Neural Independent Component Analysis (ICA) algorithms based on unimodal source distributions provide acceptable performances in the case of Blind Source Separation (BSS) of super-gaussian sources. However, their convergence profiles are significantly slower in the case of sub-gaussian sources. In some situations it is necessary to deal with sub-gaussian(More)
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