Blind Separation for Post-nonlinear Mixture of Sub- and Super-Gaussian Signals
Zhenya He · 2001
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.