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Abstract
This thesis utilize The minimum entropy or maximum likelihood estimation in blind source separation problem. Blind signal separation is the problem of separation of independent source signals from mixed observed data. Blind signal separation is a fundamental and challenging optimization problem in signal processing field. It is based on S and Weibull probability density models. A set of natural gradient blind signal separation rules is derived. This set of adaptation rules give promising results when we test sub and super Gaussian signals.