中国铁道科学2012，Vol.33Issue(6)：6-10,5.DOI:10.3969/j.issn.1001-4632.2012.06.02
基于最小二乘支持向量机的高速铁路路基沉降预测
Settlement Prediction of High-Speed Railway Subgrade Based on Least Squares Support Vector Machine
摘要
Abstract
Because the construction environment of high-speed railway subgrade is very complex, settlement monitoring data is often unequal time-interval. For Least Squares Support Vector Machine (LS-SVM) has strong nonlinear fitting capability, the equal time-interval settlement time series of subgrade were obtained by using the method of equal time-interval interpolation to a settlement-time relation function which was established by LS-SVM, and then the settlement prediction model of high-speed railway subgrade was established based on LS-SVM. The above prediction model and a combination method of BP neural network and Grey Theory were respectively used to predict subgrade settlement of 5 subgrade settlement monitoring sections at Shangyu north train station of Hangzhou-Ningbo passenger dedicated line. The comparison between prediction results and field test data shows that the accuracy of short time-interval LS-SVM prediction model is much higher and more stable than that of the combination method of BP neural network and Grey Theory, and the extrapolation prediction results of the former are more credible than those of the latter.关键词
路基/沉降预测/最小二乘支持向量机/时间序列/预测模型/高速铁路Key words
Subgrade/ Settlement prediction/ Least squares support vector machine/ Time series/ Prediction model/ High-speed railway分类
交通工程引用本文复制引用
冯胜洋,魏丽敏,郭志广..基于最小二乘支持向量机的高速铁路路基沉降预测[J].中国铁道科学,2012,33(6):6-10,5.基金项目
铁道部科技研究开发计划项目(2009G008-B,2010G018-E-3) （2009G008-B,2010G018-E-3）
