Initialisation of Nonlinearities for PNL and Wiener systems Inversion

Authors:
Jordi Sole-Casals;Christian Jutten;Dinh-Tuan Pham
Affiliations:
Signal Processing Group, Universitat de Vic, Vic, Spain 08500 and Laboratoire des Images et des Signaux (CNRS UMR n°5083), INPG, Grenoble Cedex, France 38031;Laboratoire des Images et des Signaux (CNRS UMR n°5083), INPG, Grenoble Cedex, France 38031;Laboratoire de Modélisation et de Calcul, Grenoble Cedex 9, France 38041
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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Abstract

This paper proposes a very fast method for blindly initializing a nonlinear mapping which transforms a sum of random variables. The method provides a surprisingly good approximation even when the basic assumption is not fully satisfied. The method can been used successfully for initializing nonlinearity in post-nonlinear mixtures or in Wiener system inversion, for improving algorithm speed and convergence.