Analysis for fuzzy support vector regression model

• Main Authors: Yao-Yun Liang, 梁耀云
• Other Authors: Ruey-Chyn Tsaur
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/05349418207038746097

碩士 === 淡江大學 === 管理科學研究所碩士班 === 98 === In recent years,introduce the use of SVM for multivariate fuzzy linear and nonlinear regression models with efficiency solutions. However, fuzzy support vector regression model is still complicated to slove the parameters which are all fuzzy numbers. In order to cope with this problem, we adopt the fuzzy possibilistic mean method proposed by Carlsson & Fuller (2001)which is more easily to slove fuzzy support vector regression model. According to parameters are fuzzy numbers or not, there are six kinds of models. Fnally, in data analysis, we can find forecasting vales in our proposed models are fitting very well using RMSE. It is obviously that our proposed fuzzy support vector regression model can be applied to forecast with better forecasting performance