文件名称:基于NARX神经网络的轨道垂向不平顺估计_王贵
介绍说明--下载内容均来自于网络,请自行研究使用
摘要:应用白噪声聚类经验模型分解方法(EEMD, Ensemble EMD),进行轨道一车辆系统的时频分析,
分析钢轨不平顺的波长一幅值分布及短波不平顺的分布特点。通过理论推导,得到垂向钢轨不平顺与车体垂向
加速度之间的转移函数,并由简化模型仿真结果与实验数据对比分析得出二者的相关系数在0. 8以上,表明仿
真结果与实验数据非常吻合。利用简化模型进行数值仿真,所需复数乘法次数为N(21ogN+ 1),满足实时仿真
的需要。实例所测钢轨不平顺和车体加速度的相关性分析结果表明,对加速度数据进行EEMD处理,所得结果
能反映钢轨不平顺幅值变化及所在的空间位置等信息。(important to grasp the state of track irregularity for guaranteeing the train operation safety. Due to it
is difficult to detected different bands irregularities with a single inertial amount } a method was pro-
posed to assess the vertical track irregularities based on Nonlinear Auto}egressive with exogenous
input Neural Networks(NARX).A coupling dynamics model of vertical vehicle}rack interactions
was developed with the actual measured track irregularity data from high}peed line as input to ob-
twin the simulation a plurality inertia data. Then } NARX neural network } with the normalization
simulation a plurality inertia data as the input and track irregularity as the output } was built to assess)
分析钢轨不平顺的波长一幅值分布及短波不平顺的分布特点。通过理论推导,得到垂向钢轨不平顺与车体垂向
加速度之间的转移函数,并由简化模型仿真结果与实验数据对比分析得出二者的相关系数在0. 8以上,表明仿
真结果与实验数据非常吻合。利用简化模型进行数值仿真,所需复数乘法次数为N(21ogN+ 1),满足实时仿真
的需要。实例所测钢轨不平顺和车体加速度的相关性分析结果表明,对加速度数据进行EEMD处理,所得结果
能反映钢轨不平顺幅值变化及所在的空间位置等信息。(important to grasp the state of track irregularity for guaranteeing the train operation safety. Due to it
is difficult to detected different bands irregularities with a single inertial amount } a method was pro-
posed to assess the vertical track irregularities based on Nonlinear Auto}egressive with exogenous
input Neural Networks(NARX).A coupling dynamics model of vertical vehicle}rack interactions
was developed with the actual measured track irregularity data from high}peed line as input to ob-
twin the simulation a plurality inertia data. Then } NARX neural network } with the normalization
simulation a plurality inertia data as the input and track irregularity as the output } was built to assess)
相关搜索: 轨道不平顺
(系统自动生成,下载前可以参看下载内容)
下载文件列表
基于NARX神经网络的轨道垂向不平顺估计_王贵.caj