Fast approximation of nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied. We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that speed of the algorithm is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.