Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23873
Title: The forecasting model based on fuzzy novel v-support vector machine
Authors: Wu, Q
Law, R 
Keywords: Fuzzy v-support vector machine
Triangular fuzzy number
Particle swarm optimization
Sale forecasts
Issue Date: 2011
Publisher: Pergamon Press
Source: Expert systems with applications, 2011, v. 38, no. 10, p. 12028-12034 How to cite?
Journal: Expert systems with applications 
Abstract: This paper presents a new version of fuzzy support vector machine to forecast multi-dimension fuzzy sample. By combining the triangular fuzzy theory with the modified v-support vector machine, the fuzzy novel v-support vector machine (FNv-SVM) is proposed, whose constraint conditions are less than those of the standard Fv-SVM by one, is proved to satisfy the structure risk minimum rule under the condition of probability. Moreover, there is no parameter b in the regression function of the FNv-SVM. To seek the optimal parameters of the FNv-SVM, particle swarm optimization is also proposed to optimize the unknown parameters of the FNv-SVM. The results of the application in sale forecasts confirm the feasibility and the validity of the FNv-SVM model. Compared with the traditional model, the FNv-SVM method requires fewer samples and has better forecasting precision.
URI: http://hdl.handle.net/10397/23873
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2011.01.054
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