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The complex fuzzy system forecasting model based on triangular fuzzy robust wavelet v-support vector machine
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DOI：10.1016/j.eswa.2011.04.181.png)
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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 v-support vector regression machine, a polynomial slack variable is also designed, the triangular fuzzy robust wavelet v-support vector regression machine (TFRWv-SVM) is proposed. To seek the optimal parameters of TFRWv-SVM, particle swarm optimization is also applied to optimize parameters of TFRWv-SVM. A forecasting method based on TFRWv-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, TFRWv-SVM method requires fewer samples and has better forecasting precision. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Fuzzy v-support vector machine
Wavelet kernel function
Particle swarm optimization
Fuzzy system forecasting
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IF:
7.5
论文数:
2.9W
被引数:
10.2W
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