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基于组合预测模型的轮轨力连续测试

李奕璠 林建辉 刘建新

李奕璠, 林建辉, 刘建新. 基于组合预测模型的轮轨力连续测试[J]. 西南交通大学学报, 2012, 25(4): 597-604. doi: 10.3969/j.issn.0258-2724.2012.04.010
引用本文: 李奕璠, 林建辉, 刘建新. 基于组合预测模型的轮轨力连续测试[J]. 西南交通大学学报, 2012, 25(4): 597-604. doi: 10.3969/j.issn.0258-2724.2012.04.010
LI Yifan, LIN Jianhui, LIU Jianxin. Wheel-Rail Force Continuous Measurement Based on Combinational Forecast Model[J]. Journal of Southwest Jiaotong University, 2012, 25(4): 597-604. doi: 10.3969/j.issn.0258-2724.2012.04.010
Citation: LI Yifan, LIN Jianhui, LIU Jianxin. Wheel-Rail Force Continuous Measurement Based on Combinational Forecast Model[J]. Journal of Southwest Jiaotong University, 2012, 25(4): 597-604. doi: 10.3969/j.issn.0258-2724.2012.04.010

基于组合预测模型的轮轨力连续测试

doi: 10.3969/j.issn.0258-2724.2012.04.010
基金项目: 

国家863计划资助项目(2009AA11Z202)

国家自然科学基金资助项目(51075340)

第12届高等院校青年教师基金基础 性研究课题(121075)

详细信息
    通讯作者:

    林建辉(1964-),男,教授,主要研究方向为测控技术、故障诊断、信号分析与处理,E-mail:lin13008104673@126.com

Wheel-Rail Force Continuous Measurement Based on Combinational Forecast Model

  • 摘要: 为了精确判断车辆的运行状态,提出了一种轮轨力连续测试方法.根据轮轨相互作用的特点,采用阈值判断法从测试数据中提取轮轨力的有效信息.针对轮轨力测试系统的时变性和不确定性,将动态测试序列作为灰色过程处理,提出用灰色理论对轮轨力进行连续测试.为了提高预测精度,结合遗传算法和神经网络对传统的GM(1,1)模型进行改进.建立了10个预测模型分别进行预测,然后将精度较高的预测值输入串联灰色神经网络进行二次预测,以提高预测精度与稳定性.将这10个预测模型应用到轮轨力连续测试中,结果表明:灰色系统、遗传算法与神经网络三者的组合模型具有较高的精度,平均相对误差不超过2%,满足轮轨力连续测试的要求,并且能够降低传感器失效对测试结果的影响.

     

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出版历程
  • 收稿日期:  2011-12-27
  • 刊出日期:  2012-08-25

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