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Initialisation of Nonlinearities for PNL and Wiener systems Inversion
Universitat de Vic. Escola Politècnica Superior; Universitat de Vic. Grup de Recerca en Tecnologies Digitals; International Work-Conference on Artificial and Natural Neural Networks (7a : 2003 : Maó, Menorca, Illes Balears)
This paper proposes a very fast method for blindly initial- izing 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 satis¯ed. The method can been used success- fully for initializing nonlinearity in post-nonlinear mixtures or in Wiener system inversion, for improving algorithm speed and convergence.
Xarxes neuronals (Informàtica)
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