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

Authors:
Qi Wu;Rob Law
Affiliations:
Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing, Jiangsu 210096, China and School of Hotel and Tourism Management, Hon ...;School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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 @n-support vector machine, the fuzzy novel @n-support vector machine (FN@n-SVM) is proposed, whose constraint conditions are less than those of the standard F@n-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 FN@n-SVM. To seek the optimal parameters of the FN@n-SVM, particle swarm optimization is also proposed to optimize the unknown parameters of the FN@n-SVM. The results of the application in sale forecasts confirm the feasibility and the validity of the FN@n-SVM model. Compared with the traditional model, the FN@n-SVM method requires fewer samples and has better forecasting precision.