Nonlinear robust modeling base on least trimmed squares regression

Author

Xin, Bao ; Liankui, Dai

Author_Institution

State Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou

fYear

2008

fDate

25-27 June 2008

Firstpage

5360

Lastpage

5365

Abstract

Due to low breakdown point of existing nonlinear robust modeling algorithms, a novel robust modeling algorithm based on least trimmed squares is proposed. This algorithm is based on linear least trimmed squares regression. Confidence interval of normal distribution is used to select outliers, and least square support vector machine regression is applied for nonlinear modeling. Simulation results show the breakdown point for the algorithm can exceed 45%, and it is more sensitive in outlier detection than other nonlinear robust modeling algorithms.

Keywords

least squares approximations; modelling; normal distribution; regression analysis; support vector machines; least trimmed squares regression; nonlinear robust modeling; normal distribution; outlier detection; support vector machine; Automation; Electric breakdown; Gaussian distribution; Industrial control; Intelligent control; Least squares approximation; Least squares methods; Robust control; Robustness; Support vector machines; breakdown point; least square support vector machine; least trimmed squares; nonlinear; robust modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4594542

Filename

4594542