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Lookup NU author(s): Dr Wanqing ZhaoORCiD
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The Takagi-Sugeno-Kang-type rule-based fuzzy model has found many applications in different fields; a major challenge is, however, to build a compact model with optimized model parameters which leads to satisfactory model performance. To produce a compact model, most existing approaches mainly focus on selecting an appropriate number of fuzzy rules. In contrast, this paper considers not only the selection of fuzzy rules but also the structure of each rule premise and consequent, leading to the development of a novel compact rule-based fuzzy model. Here, each fuzzy rule is associated with two sets of input attributes, in which the first is used for constructing the rule premise and the other is employed in the rule consequent. A new hybrid learning method combining the modified harmony search method with a fast recursive algorithm is hereby proposed to determine the structure and the parameters for the rule premises and consequents. This is a hard mixed-integer nonlinear optimization problem, and the proposed hybrid method solves the problem by employing an embedded framework, leading to a significantly reduced number of model parameters and a small number of fuzzy rules with each being as simple as possible. Results from three examples are presented to demonstrate the compactness (in terms of the number of model parameters and the number of rules) and the performance of the fuzzy models obtained by the proposed hybrid learning method, in comparison with other techniques from the literature. © 2013 IEEE.
Author(s): Zhao W, Niu Q, Li K, Irwin GW
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Cybernetics
Year: 2013
Volume: 43
Issue: 6
Pages: 1807-1821
Print publication date: 01/12/2013
Online publication date: 10/01/2013
ISSN (print): 2168-2267
ISSN (electronic): 168-2275
Publisher: IEEE
URL: https://doi.org/10.1109/TSMCB.2012.2231068
DOI: 10.1109/TSMCB.2012.2231068
PubMed id: 23757574
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