T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm

This paper proposes a novel approach for identification of Takagi–Sugeno (T–S) fuzzy model, which is based on a new fuzzy c-regression model (FCRM) clustering algorithm. The clustering prototype in fuzzy space partition is hyper-plane, so FCRM clustering technique is more suitable to be applied in p...

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Vydáno v:Engineering applications of artificial intelligence Ročník 22; číslo 4; s. 646 - 653
Hlavní autoři: Li, Chaoshun, Zhou, Jianzhong, Xiang, Xiuqiao, Li, Qingqing, An, Xueli
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.06.2009
Témata:
Fuzzy c-means
Fuzzy c-regressive model
Fuzzy modeling
Orthogonal least squares
System identification
System identification
Fuzzy modeling
Orthogonal least squares
Fuzzy c-regressive model
Fuzzy c-means
ISSN:0952-1976, 1873-6769
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Popis
Shrnutí:This paper proposes a novel approach for identification of Takagi–Sugeno (T–S) fuzzy model, which is based on a new fuzzy c-regression model (FCRM) clustering algorithm. The clustering prototype in fuzzy space partition is hyper-plane, so FCRM clustering technique is more suitable to be applied in premise parameters identification of T–S fuzzy model. A new FCRM clustering algorithm (NFCRMA) is presented, which is deduced from the fuzzy clustering objective function of FCRM with Lagrange multiplier rule, possessing integrative and concise structure. The proposed approach consists mainly of two steps: premise parameter identification and consequent parameter identification. The NFCRMA is utilized to partition the input–output data and identify the premise parameters, which can discover the real structure of the training data; on the other hand, orthogonal least square is exploited to identify the consequent parameters. Finally, some examples are given to verify the validity of the proposed modeling approach, and the results show the new approach is very efficient and of high accuracy.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2009.02.003