Subgrade Settlement Prediction Based on Support Vector Machine

摘要：

Due to traditional ballastless track settlement prediction algorithms have large error and cant accurately forecast settlement after work, a new method using Support Vector Machine(SVM) to forecast ballastless track settlement of high-speed railway is proposed in this paper. Firstly, build a SVM model and calculate the dual model. Then, mapping it to a higher dimension space by kernel function. At last solve and validate the model by an example. Bj comparing with the traditional forecasting algorithms and BP neural network, the results show that SVM can obtain high prediction precision and good generalization capability in few training samples comparing to other algorithms, provide a more secure and reliable solution for ballastless track settlement.

关键词： Support Vector Machine(SVM) ballastless track style subgrade settlement

作者: Chuntao Man Shun Wang Wei Wang Juanjuan Zhao

作者单位: Department of Automation,Harbin University of Science and Technology,China

会议类型: 国际会议

会议名称: The 6th International Forum on Strategic Technology(IFOST 2011)(第六届国际战略技术论坛)

会议地点: 哈尔滨

会议语种:英文

页码: 971-974

在线出版日期: 2011-08-22（万方平台首次上网日期，不代表论文的发表时间）