Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21046
Title: The complex fuzzy system forecasting model based on fuzzy SVM with triangular fuzzy number input and output
Authors: Wu, Q
Law, R 
Keywords: Fuzzy v-support vector machine
Wavelet kernel function
Particle swarm optimization
Fuzzy system forecasting
Issue Date: 2011
Publisher: Pergamon Press
Source: Expert systems with applications, 2011, v. 38, no. 10, p. 12085-12093 How to cite?
Journal: Expert systems with applications 
Abstract: This paper presents a new version of fuzzy support vector machine to forecast the nonlinear fuzzy system with multi-dimensional input variables. The input and output variables of the proposed model are described as triangular fuzzy numbers. Then by integrating the triangular fuzzy theory and v-support vector regression machine, the triangular fuzzy v-support vector machine (TFv-SVM) is proposed. To seek the optimal parameters of TFv-SVM, particle swarm optimization is also applied to optimize parameters of TFv-SVM. A forecasting method based on TFv-SVRM and PSO are put forward. The results of the application in sale system forecasts confirm the feasibility and the validity of the forecasting method. Compared with the traditional model, TFv-SVM method requires fewer samples and has better forecasting precision.
URI: http://hdl.handle.net/10397/21046
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2011.02.094
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