RESEARCH ON FREIGHT TRAFFIC FORECAST BASED ON WAVELET AND SUPPORT VECTOR MACHINE
This paper first carries on the elaboration to the least squares support vector machine (LS-SVM) forecast model.Basing on the theory of wavelet frame and the condition of the SVM kernel function, a method that generates wavelet kernel function of the support vector machine is proposed. Then the Mexican Hat wavelet is been selected to construct LS-SVM kernel function and form LS-SVM model based on the wavelet kernel function (the LS-WSVM model), after that forecast freight traffic of highway by this model in China. Through the contrast of forecast result between four different kernel functions, it indicated that the model using wavelet kernel function have a higher validity than that of other kernel functions. At the same time, contrasting the results between LS-WSVM forecast and other forecast methods, it also indicated the LS-WSVM is able to increase the forecast precision. After taking the model into different areas of china,we find that the model has the higher application value.
LS-SVM wavelet frame kernel function freight traffic of highway
BIN-SHENG LIU YI-JUN LI ZHAN-WEN XING YU-PENG HOU XUE-SHEN SUI
School of Management, Harbin Engineering University, Harbin 150001, China;School of Management, Harb School of Management, Harbin Engineering University, Harbin 150001, China Traffic Office of The Inner Mongolia Autonomous Region, Huhehot 010020, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2524-2530
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）