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

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 show that speed of the algorithm is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.
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Contributor : Christian Jutten <>
Submitted on : Tuesday, April 28, 2009 - 4:50:06 PM
Last modification on : Friday, September 6, 2019 - 3:00:06 PM
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Jordi Solé, Christian Jutten, Dinh-Tuan Pham. Fast Approximation of nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems. Signal Processing, Elsevier, 2005, 85 (9), pp.1780-1786. ⟨10.1016/j.sigpro.2004.11.030⟩. ⟨hal-00379453⟩

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