小波神经网络在高铁路基变形分析中应用研究  

The Research of Wavelet Neural Network Applied to High-Speed Railway Subgrade Deformation Analysis

作　　者：陈冠宇[1] 李文广[1] 

机构地区：[1]浙江省测绘大队,浙江杭州310030

出　　处：《测绘与空间地理信息》2015年第9期51-53,56,共4页Geomatics & Spatial Information Technology

基　　金：广西研究生教育创新计划项目(YCSZ2014151)资助

摘　　要：路基是高速铁路的轨道基础,是整个线路结构中最为薄弱的环节,对线路的平顺性、稳定性特别敏感,加强对高铁路基的沉降变形分析是确保路基工程施工质量和保障运营安全的重要环节。引入小波神经网络组合模型应用到高铁路基的沉降变形分析中,通过工程实例分析表明,小波神经网络组合模型预测精度较BP神经网络模型高,在高铁路基的沉降变形分析中具有更好的优越性和应用效果。Subgrade is the basis of high speed railway track, is the most weak link in the whole structure, particularly sensitive to smooth lines, stability, strengthen the base of high speed railway subgrade engineering settlement deformation analysis is to ensure that the important hnk of the construction quality and operation safety. The wavelet neural network combination model was introduced and applied to the subgrade of high speed railway subsidence deformation analysis, through the analysis of engineering examples show that the wavelet neural network combination model prediction accuracy than BP neural network model is high, and has better advantages and application effect in the settlement deformation analysis of the high speed railway subgrade.

关 键 词：BP神经网络 小波神经网络 组合模型 变形分析 

分 类 号：P25[天文地球—测绘科学与技术] TU196[建筑科学—建筑理论]