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An efficient LS-SVM based method for fuzzy system construction

Zhao, Wanqing, Zhang, Jingjing and Li, Kang 2015. An efficient LS-SVM based method for fuzzy system construction. IEEE Transactions on Fuzzy Systems 23 (3) , pp. 627-643. 10.1109/TFUZZ.2014.2321594

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Abstract

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well-known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process, however the model sparseness is lost which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle non-sparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in the paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVMbased fuzzy systems but also significantly reduce the amount of computational effort in model training. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords: Fuzzy systems; Least-squares SVMs; Fuzzy rules; Efficient learning; Sparsene
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1063-6706
Funders: EPSRC
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 19 March 2014
Last Modified: 11 May 2020 09:00
URI: http://orca-mwe.cf.ac.uk/id/eprint/64624

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