Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17311
Title: Fuzzy support vector regression machine with penalizing Gaussian noises on triangular fuzzy number space
Authors: Wu, Q
Law, R 
Keywords: Fuzzy v-support vector machine
Triangular fuzzy number
Genetic algorithm
Sale forecasts
Gaussian loss function
Issue Date: 2010
Publisher: Pergamon Press
Source: Expert systems with applications, 2010, v. 37, no. 12, p. 7788-7795 How to cite?
Journal: Expert systems with applications 
Abstract: In view of the shortage of epsilon-insensitive loss function for Gaussian noise, this paper presents a new version of fuzzy support vector machine (SVM) which can penalize Gaussian noise to forecast fuzzy nonlinear system. Since there exist some problems of finite samples and uncertain data in many forecasting problem, the input variables are described as crisp numbers by fuzzy comprehensive evaluation. To represent the fuzzy degree of these input variables, the symmetric triangular fuzzy technique is adopted. Then by the integration of the fuzzy theory, v-SVM and Gaussian loss function theory, the fuzzy v-SVM with Gaussian loss function (Fg-SVM) which can penalize Gaussian noise is proposed. To seek the optimal parameters of Fg-SVM, genetic algorithm is also proposed to optimize the unknown parameters of Fg-SVM. The results of the application in sale system forecasts confirm the feasibility and the validity of the Fg-SVM model. Compared with the traditional model, Fg-SVM method requires fewer samples and has better generalization capability for Gaussian noise.
URI: http://hdl.handle.net/10397/17311
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2010.04.061
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