A novel clustering method for fuzzy model identification

Author

Tushir, Meena ; Srivastava, Smriti

Author_Institution

Deptt. of Electr. & Electron. Eng., MSIT, New Delhi, India

fYear

2009

fDate

23-26 Jan. 2009

Firstpage

1

Lastpage

5

Abstract

Takagi-Sugeno models are an important class of fuzzy rule based oriented models, generally used for prediction and control. Fuzzy clustering is one of effective methods for identification. In this method, we propose to use a fuzzy clustering method (Kernel based fuzzy c-means method) for automatically constructing a multi-input fuzzy model to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, among them a model of a human operator´s control action and a qualitative model to explain the trends in the time series data of the price of a stock.

Keywords

fuzzy control; identification; pattern clustering; statistical analysis; Takagi-Sugeno models; fuzzy clustering; fuzzy model identification; kernel based fuzzy c-means method; stock pricing; time series; Automatic control; Clustering algorithms; Clustering methods; Fuzzy control; Fuzzy systems; Kernel; Parameter estimation; Predictive models; System identification; Takagi-Sugeno model; TS Models; kernel function; system identification;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2009 - 2009 IEEE Region 10 Conference

Conference_Location

Singapore

Print_ISBN

978-1-4244-4546-2

Electronic_ISBN

978-1-4244-4547-9

Type

conf

DOI

10.1109/TENCON.2009.5395882

Filename

5395882