Fuzzy neural modeling via clustering and support vector machines

Author

Tovar, Julio César ; Yu, Wen

Author_Institution

Dept. de Control Automatico, CINVESTAV-IPN, Mexico City

fYear

2007

fDate

1-3 Oct. 2007

Firstpage

24

Lastpage

29

Abstract

This paper describes a novel fuzzy rule-based modeling approach for some industrial processes. Structure identification is realized by clustering and support vector machines. When the process is slow, fuzzy rules can be obtained automatically. Parameters identification uses the techniques of fuzzy neural networks. A time-varying learning rate assures stability of the modeling error.

Keywords

fuzzy control; fuzzy neural nets; fuzzy reasoning; learning (artificial intelligence); learning systems; neurocontrollers; nonlinear control systems; parameter estimation; process control; stability; statistical analysis; support vector machines; time-varying systems; clustering method; fuzzy neural modeling; fuzzy neural network techniques; fuzzy rule-based modeling approach; industrial process; modeling error stability; nonlinear system modeling; parameter identification; structure identification; support vector machines; time-varying learning rate; Data mining; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Neural networks; Nonlinear systems; Parameter estimation; Quadratic programming; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Applications, 2007. CCA 2007. IEEE International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-0442-1

Electronic_ISBN

978-1-4244-0443-8

Type

conf

DOI

10.1109/CCA.2007.4389200

Filename

4389200