The forecasting model based on fuzzy novel v-support vector machine

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. (C) 2011 Elsevier Ltd. All rights reserved.