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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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 43; no. 6; pp. 1807 - 1821
Main Authors: Wanqing Zhao, Qun Niu, Kang Li, Irwin, George W.
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TSMCB.2012.2231068