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Wavelet‐based adaptive robust M‐estimator for nonlinear system identification
Author(s) -
Wang D.,
Romagnoli J. A.,
Safavi A. A.
Publication year - 2000
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690460812
Subject(s) - estimator , nonlinear system , wavelet , monte carlo method , mathematics , nonparametric statistics , identification (biology) , system identification , mathematical optimization , algorithm , computer science , statistics , artificial intelligence , data mining , physics , botany , quantum mechanics , biology , measure (data warehouse)
A wavelet‐based robust M‐estimation method for the identification of nonlinear systems is proposed. Because it is not based on the assumption that there is the class of error distribution, it takes a flexible, nonparametric approach and has the advantage of directly estimating the error distribution from the data. This M‐estimator is optimal over any error distribution in the sense of maximum likelihood estimation. A Monte‐Carlo study on a nonlinear chemical engineering example was used to compare the results with various previously utilized methods.