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Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems
 
Solé-Casals, Jordi ; Jutten, Christian ; Pham, Dinh-Tuan
2005

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Enllaç permanent: http://hdl.handle.net/10854/2089
 
Versió de l'editor: https://doi.org/10.1016/j.sigpro.2004.11.030
 

Abstract

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 ...»»»»
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 the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.^^^^

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(c) Elsevier, 2005
 
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Citation:

SOLÉ CASALS, Jordi; JUTTEN, C.; PHAM, D. T. "Fast approximation of nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems". A: Signal Processing, 2005, vol. 85, núm. 9, pàg. 1780-1786.