A new approach to fuzzy modeling

Author

Kim, Euntai ; Park, Minkee ; Ji, Seunghwan ; Park, Mignon

Author_Institution

Dept. of Electron. Eng., Yonsei Univ., Seoul, South Korea

Volume

5

Issue

3

fYear

1997

fDate

8/1/1997 12:00:00 AM

Firstpage

328

Lastpage

337

Abstract

This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno´s model (1985), because it has the same structure as that of Takagi and Sugeno´s model. It is also as easy to implement as Sugeno and Yasukawa´s model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawa´s model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm

Keywords

fuzzy set theory; identification; modelling; statistical analysis; coarse tuning; fine tuning; fuzzy C-means; fuzzy C-regression model clustering; fuzzy modeling; fuzzy rules; gradient descent algorithm; identification; unknown system; Application software; Clustering algorithms; Equations; Fuzzy sets; Fuzzy systems; Humans; Microwave integrated circuits; Optimization methods; Pattern recognition; Sections;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.618271

Filename

618271