Initialisation of Nonlinearities for PNL and Wiener systems Inversion

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.