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An adaptive robust M-estimator for nonparametric nonlinear system identification

✍ Scribed by Xianchun Wu; Ali Çinar

Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
650 KB
Volume
6
Category
Article
ISSN
0959-1524

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✦ Synopsis

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 procedure's excellent performance characteristics are illustrated in a Monte Carlo study by comparing the results with previous methods.

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