z-logo
Premium
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.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here