A novel cluster validity criterion for the bilinear models and its application to the T-S fuzzy bilinear model identification

Author

Ku, Hong-Chi ; Kung, Chung-Chun ; Chen, Wei-Yin

Author_Institution

Dept. of Electr. Eng., Tatung Univ., Taipei, Taiwan

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

7

Abstract

The objective of this paper is to use the T-S fuzzy bilinear model to describe nonlinear system with high accuracy and with as fewer IF-THEN rules as possible via input/output data which collect from original nonlinear system and fuzzy c-regression models (FCRM) clustering algorithm applied to bilinear models. A novel cluster validity criterion suitable for the bilinear models will be presented. The simulation example is provided to demonstrate the accuracy of the fuzzy bilinear model.

Keywords

fuzzy set theory; identification; nonlinear control systems; pattern clustering; regression analysis; IF-THEN rules; T-S fuzzy bilinear model identification; bilinear models; fuzzy c-regression models clustering algorithm; input/output data; nonlinear system; novel cluster validity criterion; Autoregressive processes; Clustering algorithms; Data models; Mathematical model; Nonlinear systems; Partitioning algorithms; Vectors; T-S fuzzy bilinear model; cluster validity criterion; fuzzy c-regression models (FCRM);

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on

Conference_Location

Brisbane, QLD

ISSN

1098-7584

Print_ISBN

978-1-4673-1507-4

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZ-IEEE.2012.6250841

Filename

6250841