首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于混沌理论的铁路客货运量预测研究
引用本文:朱子虎,翁振松.基于混沌理论的铁路客货运量预测研究[J].铁道学报,2011,33(6):1-7.
作者姓名:朱子虎  翁振松
作者单位:铁道部经济规划研究院运输咨询部,北京,100038
基金项目:铁道部科技研究开发计划
摘    要:应用混沌理论的相空间重构方法,分析与铁路运量相关的12组时间序列,分别计算它们的嵌入延迟时间、嵌入维数、关联维数、最大Lyapunov指数等混沌统计量,并以此为依据判断12组时间序列的混沌特性。结果显示:铁路客货运量及周转量不具有混沌特性,对应的4组时间序列不是混沌的;铁路客货运量、周转量的增量及增长率都具有明显的混沌特性,它们对应的8组时间序列是混沌的。在识别是否混沌的基础上,应用基于最大Lyapunov指数预测方法,对铁路客货运量、周转量进行预测检验及预测结果分析。

关 键 词:铁路运量预测  混沌理论  相空间重构  时间延迟  嵌入维数  最大Lyapunov指数

Railway Passenger and Freight Volume Forecasting Based on Chaos Theory
ZHU Zi-hu,WENG Zhen-song.Railway Passenger and Freight Volume Forecasting Based on Chaos Theory[J].Journal of the China railway Society,2011,33(6):1-7.
Authors:ZHU Zi-hu  WENG Zhen-song
Institution:ZHU Zi-hu,WENG Zhen-song(Department of Traffic Volume Planning,Economic and Planning Research Institute of the Ministry of Railway,Beijing 100038,China)
Abstract:Applying the phase space reconstruction method of the chaos theory,12 groups of time series associated with rail traffic volumes were analyzed in respect of the chaotic statistics data of the embedding delay time,embedding dimension,correlation dimension and the largest Lyapunov exponent.In accordance,the chaotic characteristics of the 12 groups of time series were identified.The analytical results show as follows: The railway passenger and freight traffic volumes and turnovers do not possess chaotic charac...
Keywords:railway traffic volume forecasting  chaos theory  phase space reconstruction  embedding delay time  embedding dimension  largest Lyapunov exponent  
本文献已被 CNKI 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号