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

• Published in: Engineering applications of artificial intelligence Vol. 22; no. 4; pp. 646 - 653
• Main Authors: Li, Chaoshun, Zhou, Jianzhong, Xiang, Xiuqiao, Li, Qingqing, An, Xueli
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2009
• Subjects: 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
• DOI: 10.1016/j.engappai.2009.02.003

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.