Abstract Compared with the natural gradient learning algorithm of blind source separation, the nonholonomic natural gradient algorithm avoids numerical instability which is caused by the nonstationary source signals and the rapid magnitude change. Aiming at the difficulty in determining the nonlinear activation function, an improved algorithm using kurtosis to select activation function adaptively without available prior information is proposed. It retains the predominance of nonholonomic natural gradient algorithm in restoring nonstationary sources, and can be adapted to the sources of arbitrary distribution. Computer simulations show the performance of proposed method is better than that of the original algorithm with tangent function.
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2006, Vol. 19 
