Efficient Nonlinear Wiener Model Identification Using a Complex-Valued Simplicial Canonical Piecewise Linear Filter

Authors:
J.E. Cousseau;J.L. Figueroa;S. Werner;T.I. Laakso
Affiliations:
CONICET-Dept. of Electr. & Comput. Eng., Univ. Nacional del Sur, Bahia Blanca;-;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
2007

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Adaptive kernel canonical correlation analysis algorithms for nonparametric identification of Wiener and Hammerstein systems

EURASIP Journal on Advances in Signal Processing

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Abstract

This paper proposes an efficient adaptive realization of the Wiener model for the identification of complex-valued nonlinear systems. Using a two-dimensional simplicial canonical piecewise linear filter for the complex-valued nonlinear mapping, we derive a realization of the Wiener model requiring fewer parameters than previous approaches. An adaptive implementation of the proposed Wiener model is derived, and local convergence analysis for the updating algorithm is presented. The tradeoff between computational complexity and modeling performance is discussed. Simulations of a system identification example show that the proposed algorithm can provide similar or better performance than other approaches in terms of computational complexity, convergence speed, and final mean-squared error (MSE)