A Hybrid Learning Method for Constructing Compact Rule-Based Fuzzy Models

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...

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Vydané v:	IEEE transactions on cybernetics Ročník 43; číslo 6; s. 1807 - 1821
Hlavní autori:	Wanqing Zhao, Qun Niu, Kang Li, Irwin, George W.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.12.2013 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Algorithms Artificial Intelligence Compact rule-based systems Computer Simulation Decision Support Techniques fast recursive algorithm (FRA) Fuzzy Logic fuzzy rules Fuzzy sets fuzzy structure Fuzzy systems harmony search (HS) Learning systems Models, Statistical Optimization Pattern Recognition, Automated - methods Polynomials Teaching methods Vectors
ISSN:	2168-2267, 2168-2275, 2168-2275
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Abstract	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.
AbstractList	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.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. 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.
Author	Kang Li Qun Niu Wanqing Zhao Irwin, George W.
Author_xml	– sequence: 1 surname: Wanqing Zhao fullname: Wanqing Zhao email: wzhao02@qub.ac.uk organization: Sch. of Electron., Electr. Eng. & Comput. Sci., Queen's Univ. Belfast, Belfast, UK – sequence: 2 surname: Qun Niu fullname: Qun Niu email: comelycc@gmail.com organization: Sch. of Mechatron. & Autom., Shanghai Univ., Shanghai, China – sequence: 3 surname: Kang Li fullname: Kang Li email: k.li@qub.ac.uk organization: Sch. of Electron., Electr. Eng. & Comput. Sci., Queen's Univ. Belfast, Belfast, UK – sequence: 4 givenname: George W. surname: Irwin fullname: Irwin, George W. email: G.Irwin@qub.ac.uk organization: Sch. of Electron., Electr. Eng. & Comput. Sci., Queen's Univ. Belfast, Belfast, UK
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SubjectTerms	Accuracy Algorithms Artificial Intelligence Compact rule-based systems Computer Simulation Decision Support Techniques fast recursive algorithm (FRA) Fuzzy Logic fuzzy rules Fuzzy sets fuzzy structure Fuzzy systems harmony search (HS) Learning systems Models, Statistical Optimization Pattern Recognition, Automated - methods Polynomials Teaching methods Vectors
Title	A Hybrid Learning Method for Constructing Compact Rule-Based Fuzzy Models
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