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 ν-support vector machine, the fuzzy novel ν-support vector machine (FNν-SVM) is proposed, whose constraint conditions are less than those of the standard Fν-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 FNν-SVM. To seek the optimal parameters of the FNν-SVM, particle swarm optimization is also proposed to optimize the unknown parameters of the FNν-SVM. The results of the application in sale forecasts confirm the feasibility and the validity of the FNν-SVM model. Compared with the traditional model, the FNν-SVM method requires fewer samples and has better forecasting precision.
- Fuzzy ν-support vector machine
- Particle swarm optimization
- Sale forecasts
- Triangular fuzzy number
ASJC Scopus subject areas
- Computer Science Applications
- Artificial Intelligence