1384189

11082

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.

233

M-estimator , System identification , Nonlinear system

Studia Iranica

239

239