A hybrid clustering and gradient descent approach for fuzzy modeling

Author

Wong, Ching-Chang ; Chen, Chia-Chong

Author_Institution

Dept. of Electr. Eng., Tamkang Univ., Tamsui, Taiwan

Volume

29

Issue

6

fYear

1999

fDate

12/1/1999 12:00:00 AM

Firstpage

686

Lastpage

693

Abstract

In this paper, a hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed approach is composed of two steps: structure identification and parameter identification. In the process of structure identification, a clustering method is proposed to provide a systematic procedure to determine the number of fuzzy rules and construct an initial fuzzy model from the given input-output data. In the process of parameter identification, the gradient descent method is used to tune the parameters of the constructed fuzzy model to obtain a more precise fuzzy model from the given input-output data. Finally, two examples of nonlinear system are given to illustrate the effectiveness of the proposed approach

Keywords

fuzzy logic; nonlinear systems; parameter estimation; fuzzy modeling; gradient descent approach; hybrid clustering; input-output data; multi-input fuzzy model; nonlinear system; parameter identification; structure identification; Clustering algorithms; Clustering methods; Fuzzy sets; Fuzzy systems; Inference algorithms; Mathematical model; Nonlinear systems; Parameter estimation; System identification; Uncertain systems;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/3477.809024

Filename

809024