A Clustering-Based Method for Fuzzy Modeling
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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Ching-Chang WONG, Chia-Chong CHEN, "A Clustering-Based Method for Fuzzy Modeling" in IEICE TRANSACTIONS on Information,
vol. E82-D, no. 6, pp. 1058-1065, June 1999, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e82-d_6_1058/_p
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@ARTICLE{e82-d_6_1058,
author={Ching-Chang WONG, Chia-Chong CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={A Clustering-Based Method for Fuzzy Modeling},
year={1999},
volume={E82-D},
number={6},
pages={1058-1065},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={June},}
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TY - JOUR
TI - A Clustering-Based Method for Fuzzy Modeling
T2 - IEICE TRANSACTIONS on Information
SP - 1058
EP - 1065
AU - Ching-Chang WONG
AU - Chia-Chong CHEN
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E82-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 1999
AB - 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.
ER -
English
