The complex fuzzy system forecasting model based on triangular fuzzy robust wavelet υ-support vector machine

Authors:
Qi Wu
Affiliations:
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, Jiangsu 210096, China and School of Hotel and Tourism Management, ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

This paper presents a new version of fuzzy wavelet support vector regression machine to forecast the nonlinear fuzzy system with multi-dimensional input variables. The input and output variables of the proposed model are described as triangular fuzzy numbers. Then by integrating the triangular fuzzy theory, wavelet analysis theory and @n-support vector regression machine, a polynomial slack variable is also designed, the triangular fuzzy robust wavelet @n-support vector regression machine (TFRW@n-SVM) is proposed. To seek the optimal parameters of TFRW@n-SVM, particle swarm optimization is also applied to optimize parameters of TFRW@n-SVM. A forecasting method based on TFRW@n-SVRM and PSO are put forward. The results of the application in sale system forecasts confirm the feasibility and the validity of the forecasting method. Compared with the traditional model, TFRW@n-SVM method requires fewer samples and has better forecasting precision.