Training Method of Support Vector Regression Based on Multi-Dimensional Feature and Research on Forecast Model of Vibration Time Series
In recent years, Support Vector Regression (SVR) is used widely in predication field, with the advantages of structural risk minimization and strong generalization ability, which acquires good effects. The training characters of SVR model is the essential problem of affecting model accuracy. To solve the problem, this paper puts forward SVR model training method based on wavelet multi-resolution analysis, which adopts wavelet multi-resolution analysis to decompose time sequence and then uses the components data of each time spot as features to train SVR. The experiments has proved that the SVR training method which combines dynamic features of time series and detail information can improve the accuracy of the prediction model.
support vector regression wavelet multi resolution analysis feature extraction vibration forecast
Han Zhonghe Zhu Xiaoxun Yang Xiaojing
North China Electric Power University, School of Energy and Power Engineering, Baoding, Hebei, 071003, China
国际会议
长沙
英文
3449-3452
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）