计算机科学 ›› 2017, Vol. 44 ›› Issue (10): 91-95.doi: 10.11896/j.issn.1002-137X.2017.10.017

• 网络与通信 • 上一篇    下一篇

CRH2型动车组列车信息传输网络流量建模与预测

葛诗春,刘雄飞,周锋   

  1. 中南大学物理与电子学院 长沙410006,中南大学物理与电子学院 长沙410006,中南大学物理与电子学院 长沙410006
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金资助

Modeling and Prediction on Train Communication Network Traffic of CRH2 EMUs

GE Shi-chun, LIU Xiong-fei and ZHOU Feng   

  • Online:2018-12-01 Published:2018-12-01

摘要: 针对CRH2型动车组列车网络流量数据日益复杂的特性,提出了一种将主成分分析法(PCA)与后馈神经网络(BP网络)相结合的网络流量建模预测思路。基于已搭建好的CRH2型列车通信仿真平台,对该仿真网络各条链路进行流量采集。为了降低分析的复杂度,流量数据先进行PCA降维预处理分析,再将数据输入到BP神经预测网络模型进行仿真预测。经验证,该思路能有效拟合列车主体网络流量的变化趋势,为CRH2型动车组通信网络的故障诊断分析提供了一定的参考。

关键词: CRH2型动车组,主成分分析,后馈神经网络,流量预测,故障诊断

Abstract: Aiming at the increasing complexity of the CRH2 train network traffic data,the method based on principal component analysis (PCA) and back propagation neural network (BP Network) was proposed to model and predict network traffic.Based on the built CRH2 train communication simulation platform,traffic of various links of the network has been collected.In order to reduce the complexity of analysis,the dimension reduction analysis is carried out with the application of PCA,then the data is input to BP network for simulation prediction.It is proved that the method can effectively fit the trend of the train network flow,providing concrete reference for the fault diagnosis of CRH2 train communication network.

Key words: CRH2 type EMUs,Principal component analysis,Back propagation neural network,Traffic prediction,Fault diagnose

[1] LIU L,JIANG H H,WANG J,et al.Study On Characteristics and Modling of An Industial Ethernet Traffic[J].Microelectro-nics and Computer,2015,3(5):1-5.(in Chinese)刘亮,江汉红,王洁,等.工业以太网网络流量的特性分析与建模研究[J].微电子学与计算机,2015,3(5):1-5.
[2] PENG H D,WANG H.Converter Fault Analysis of CRH2CEMU[J].Journal of the China Railway Quality Control,2013,3(1):33-35.(in Chinese) 彭华东,王慧.CRH2C型动车组牵引变流器故障分析[J].铁道技术监督,2013,3(1):33-35.
[3] LI Z L,HU G M,YAO X M,et al.Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis[J].EURASIP Journal on Advances in Signal Processing,2009(1):135-140.
[4] 张曙光.CRH2型动车组[M].北京:中国铁道出版社,2008:15-60.
[5] NIE X B,WANG L D,SHEN P,et al.Real-time Performance Research of the ARCNET control System[J].Journal of the China Railway Society,2011,3(1):58-62.(in Chinese) 聂晓波,王立德,申萍,等.ARCNET网络系统实时性能分析与研究[J].铁道学报,2011,3(1):58-62.
[6] KUANG C H,LI J W,WANG Y S,et al.Modeling and Simulation of Train Communication Network of ARCNET Based on OPNET[J].Journal of Railway Computer Application,2008,5(1):49-51.(in Chinese) 况长虹,李家武,王玉松,等.基于OPNET的ARCNET列车通信网络的建模与仿真[J].铁路计算机应用,2008,5(1):49-51.
[7] WANG M,LI C X,CHEN C J.Network Traffic Analysis Based on PCA[J].Journal of Microcomputer Information,2006,6(4):94-97.(in Chinese) 王敏,李纯喜,陈常嘉.浅谈基于PCA的网络流量分析[J].微计算机信息,2006,6(4):94-97.
[8] XIONG S S.The Theory research on Dynamics System Mode-ling of Neural Network[J].Journal of Tsinghua University(Na-tural Science),1998(8):25-30.(in Chinese) 熊沈蜀.神经网络在动力学系统建模中的理论研究[J].清华大学学报(自然科学版),1998(8):25-30.
[9] 范剑青,姚琦伟.非线性时间序列:建模、预报及应用[M].北京:高等教育出版社,2005:76-86.
[10] YUE J,BAO S Y,LI Q Z,et al.The Analysis of BP Network Traffic Accident Serious Level Model Based on Principal Component[J].Applied Mechanics and Materials,2014,3488 (641):910-915.
[11] TIAN Z D,LI S J,WANG Y H,et al.Network traffic Forecasting Method of Compensation ELM Based on ARIMA[J].Journal of Information and Control,2014,3(6):705-710.(in Chinese) 田中大,李树江,王艳红,等.基于ARIMA补偿ELM的网络流量预测方法[J].信息与控制,2014,3(6):705-710.
[12] SUN Y,BAI G W,ZHAO L.Network traffic rediction based on wavelet FARIMA model[J].Journal of Computer Applications,2011,31(4):901-903.(in Chinese) 孙勇,白光伟,赵露.基于小波分形自回归整合滑动平均模型的网络流量预测[J].计算机应用,2011,1(4):901-903.
[13] ZHANG B,YANG J H,WU J P.Analysis and Review of Internet Traffic Model[J].Journal of Software,2011,2(1):115-131.(in Chinese) 张宾,杨家海,吴建平.Internet流量模型分析与评述[J].软件学报,2011,2(1):115-131.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!