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A Clustering-Based Method for Fuzzy Modeling

Ching-Chang WONG, Chia-Chong CHEN

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Summary :

In this paper, a clustering-based method is proposed for automatically constructing a multi-input Takagi-Sugeno (TS) fuzzy model where only the input-output data of the identified system are available. The TS fuzzy model is automatically generated by the process of structure identification and parameter identification. In the structure identification step, a clustering method is proposed to provide a systematic procedure to partition the input space so that the number of fuzzy rules and the shapes of fuzzy sets in the premise part are determined from the given input-output data. In the parameter identification step, the recursive least-squares algorithm is applied to choose the parameter values in the consequent part from the given input-output data. Finally, two examples are used to illustrate the effectiveness of the proposed method.

Publication
IEICE TRANSACTIONS on Information Vol.E82-D No.6 pp.1058-1065
Publication Date
1999/06/25
Publicized
Online ISSN
DOI
Type of Manuscript
PAPER
Category
Image Processing,Computer Graphics and Pattern Recognition

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