Xianchun Wu and Ali Cinar، نويسنده ,

1384189

An adaptive robust M-estimator for nonparametric nonlinear system identification

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An adaptive robust M-estimator for nonparametric nonlinear system identification is proposed. This Mestimator is optimal over a broad class of distributions in the sense of maximum likelihood estimation. The error distributions are described by the generalized exponential distribution family. It combines nonparametric regression techniques to form a powerful procedure for nonlinear system identification. The adaptive procedureʹs excellent performance characteristics are illustrated in a Monte Carlo study by comparing the results with previous methods.

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M-estimator , System identification , Nonlinear system

Studia Iranica

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