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 ...»»»»
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.^^^^