A Clustering-Based Method for Fuzzy Modeling

概要

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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 parameteridentification step, the recursive least-squares algorithm is applied to choose the parameter values in the consequent part from the given input-outptat data. Finally, two examples are used to illustrate the effectiveness of the proposed method.
社団法人電子情報通信学会の論文
1999-06-25