An Evolutionary Approach for Blind Inversion of Wiener Systems

Abstract

The problem of blind inversion of Wiener systems can be considered as a special case of blind separation of post-nonlinear instantaneous mixtures. In this paper, we present an approach for nonlinear deconvolution of one signal using a genetic algorithm. The recovering of the original signal is achieved by trying to maximize an estimation of mutual information based on higher order statistics. Analyzing the experimental results, the use of genetic algorithms is appropriate when the number of samples of the convolved signal is low, where other gradient-like methods may fail because of poor estimation of statistics.