Initialisation of Nonlinearities for PNL and Wiener Systems Inversion
نویسندگان
چکیده
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.
منابع مشابه
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 the algorithm speed is strongly improved and the ...
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تاریخ انتشار 2003