A new two-step fuzzy inference approach based on Takagi-Sugeno inference using discrete type 2 fuzzy sets

Author

Uncu, Ozge ; Turksen, I.B.

Author_Institution

Dept. of Mech. & Ind. Eng., Toronto Univ., Toronto, Ont., Canada

fYear

2003

fDate

24-26 July 2003

Firstpage

32

Lastpage

37

Abstract

Fuzzy system modeling (FSM) is one of the most prominent tools in order to capture the hidden behavior of highly nonlinear systems with uncertainty. In this paper, a new type 2 FSM approach is proposed in order to increase the predictive power of traditional Takagi-Sugeno fuzzy system models. One of the biggest problems of type 2 fuzzy system models is computational complexity. In order to remedy this problem, the proposed inference mechanism performs type reduction as a first step. Then, the type 1 inference mechanisms are utilized to deduce a model output for a given crisp observation.

Keywords

computational complexity; fuzzy set theory; fuzzy systems; inference mechanisms; nonlinear systems; Takagi-Sugeno fuzzy system models; Takagi-Sugeno inference; computational complexity; discrete type 2 fuzzy sets; fuzzy inference mechanism; fuzzy system modeling; nonlinear systems; system uncertainty; Computational complexity; Computational modeling; Fuzzy sets; Fuzzy systems; Inference mechanisms; Nonlinear systems; Power system modeling; Predictive models; Takagi-Sugeno model; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 2003. NAFIPS 2003. 22nd International Conference of the North American

Print_ISBN

0-7803-7918-7

Type

conf

DOI

10.1109/NAFIPS.2003.1226751

Filename

1226751