AN ADAPTIVE ROBUST M-ESTIMATOR FOR NONPARAMETRIC NONLINEAR-SYSTEM IDENTIFICATION

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