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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Sole-Casals, J., Jutten, C., & Pham, D. T. (2003). Initialisation of nonlinearities for PNL and Wiener systems inversion. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2687, 225–232. https://doi.org/10.1007/3-540-44869-1_29
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