Survey of the selection moisture forecasting model feature based on support vector machine
Support Vector Machine (SVM) is a new machine learning technology created by Vladimir N Vapnki in the nineties of last century.By integrating the largest interval hyperplane, Mercer kernel, quadratic programming, sparse solutions and relaxation techniques, SVM has been proven to be a promising forecasting model with the strong generalization capability in various challenging application areas. On the basis of introducing the basic principles of Support Vector Regression Machine (SVR) and the validation of underground water forecasting with IP method, radial basis function was chosen as the kernel function prediction model; the inherent relationship between the electrical parameters and the underground aquifers was studied using the measurement data collected at Ximazhuang proving ground in the city of Shijiazhuang. Lots of electrical sounding data and underground water pumping volume were collected then the best input vectors of underground water content predicted used by IP method based on SVM were identified for further research.
support vector machine moisture forecasting coherent analysis cycle test
Zheng Hou Guohui Liu Hongwei Song Tianyi Wang Ying Yuan
Shijiazhuang University of Economics, Shijiazhuang 050031, P.R.China Shijiazhuang University of Economics,Shijiazhuang 050031, P.R.China
国际会议
成都
英文
478-482
2010-06-14（万方平台首次上网日期，不代表论文的发表时间）