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An efficient LS-SVM-based method for fuzzy system construction

Lookup NU author(s): Dr Wanqing ZhaoORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 1993-2012 IEEE. 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 the 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 this 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-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. 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.

Publication metadata

Author(s): Zhao W, Zhang J, Li K

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Fuzzy Systems

Year: 2015

Volume: 23

Issue: 3

Pages: 627-643

Print publication date: 01/06/2015

Online publication date: 02/05/2014

Acceptance date: 19/03/2014

Date deposited: 22/09/2022

ISSN (print): 1063-6706

ISSN (electronic): 1941-0034

Publisher: IEEE


DOI: 10.1109/TFUZZ.2014.2321594


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