Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A canonical genetic algorithm for blind inversion of linear channels
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Quasi-nonparametric blind inversion of Wiener systems
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hi-index | 0.00 |
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.