Modeling via on-line clustering and fuzzy support vector machines for nonlinear system

Author

Tovar, Julio César ; Yu, Wen ; Ortiz, Floriberto ; Mariaca, Carlos Román ; de Jesus Rubio, Jose

Author_Institution

Eng. Commun. & Electron. Autom. Control Dept., Nat. Inst. Polytech., Mexico City, Mexico

fYear

2011

fDate

12-15 Dec. 2011

Firstpage

8267

Lastpage

8272

Abstract

This paper describes a novel non-linear modeling approach by on-line clustering, fuzzy rules and fuzzy support vector machines. Structure identification is realized by on-line clustering method and support vector machines, and the rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, tue upper bounds of modeling errors are proven.

Keywords

fuzzy set theory; fuzzy systems; learning (artificial intelligence); learning systems; nonlinear systems; pattern clustering; support vector machines; fuzzy membership function; fuzzy rules; fuzzy support vector machine; nonlinear modeling approach; nonlinear system; online clustering; rule generation; structure identification; time-varying learning rate; Engines; Fuzzy systems; Kernel; Nonlinear systems; Support vector machines; Training; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on

Conference_Location

Orlando, FL

ISSN

0743-1546

Print_ISBN

978-1-61284-800-6

Electronic_ISBN

0743-1546

Type

conf

DOI

10.1109/CDC.2011.6161420

Filename

6161420