北京交通大学学报2012，Vol.36Issue(3)：52-56,5.
基于GM(1,1)与AR模型的轨道不平顺状态预测
Prediction of track irregularity state based on grey GM (1,1) and stochastic linear AR model
摘要
Abstract
Track geometry inspection data can reflect the change of track geometry state. It is a time series change with time and has random characteristics. In this paper, combination of a generally adaptability improved grey GM (1, 1) model with residual error correction and the stochastic linear AR model are applied to analyze track irregularity of cross level in designated point and unit section, and we find the law from the random data sequence of the track geometry state changes of cross level and predict short-term, long-term track state. The results show that the model is valid and can meet the intended accuracy.关键词
轨道不平顺/随机过程/灰色模型/时间序列/自回归Key words
track irregularity/ stochastic process/ grey model/ time series/ auto-regressive分类
交通工程引用本文复制引用
贾朝龙,徐维祥,王福田,王寒凝..基于GM(1,1)与AR模型的轨道不平顺状态预测[J].北京交通大学学报,2012,36(3):52-56,5.基金项目
国家科技支撑计划项目资助(2009BAG12A10) （2009BAG12A10）
轨道交通控制与安全国家重点实验室支撑项目资助(RCS2009ZT007) （RCS2009ZT007）
北京市科委计划项目资助(Z090506006309011) （Z090506006309011）
