Abstract: The problem of blind separation of signals in post-nonlinear mixture is addressed. The learning rules for the general post-nonlinear separation structure are derived by a maximum likelihood approach. An algorithm for blind separation of post-nonlinearly mixed sub-and super-Gaussian signals based on the results of previous work is proposed. The effectiveness of the algorithm is verified by experiments on artificial and natural signals.

Key words: blind separation, maximum likelihood, nonlinear mixture, sub-and super-Gaussian, neural networks