基于改进NARX神经网络的接触线表面不平顺与弓网接触力关联分析方法  

Correlation Analysis between Catenary Irregularities and Pantograph-Catenary Contact Forces Based on Improved NARX Neural Network

作　　者：张媛[1] 秦勇[2] 程晓卿[2] 庞学苗[3] 邢宗义[3] 

机构地区：[1]北京交通大学交通运输学院,北京100044 [2]北京交通大学轨道交通控制与安全国家重点实验室,北京100044 [3]南京理工大学机械工程学院,江苏南京210094

出　　处：《中国铁道科学》2012年第3期84-91,共8页China Railway Science

基　　金：国家自然科学基金资助项目(61074151);国家"八六三"计划项目(2009AA110302);轨道交通控制与安全国家重点实验室开放课题(RCS2009K010);南京理工大学紫金之星资助项目(2010GJPY007)

摘　　要：建立弓网耦合动力学模型,采用软件MATLAB的Simulink模块对该模型进行动态仿真,获取接触线表面不平顺和弓网接触力数据;对接触线表面不平顺和弓网接触力数据进行归一化处理后,分别作为非线性自回归(NARX)神经网络的输入和输出;对传统的贝叶斯正则化算法进行改进,并采用改进的贝叶斯正则化算法进行NARX神经网络权值修正,得到改进的NARX(NARX—IR)神经网络方法;利用NARX—IR神经网络方法进行接触线表面不平顺与弓网接触力的关联分析。采用根均方误差和相关系数,对基于LM算法的BP(BP—LM)神经网络方法、基于传统贝叶斯正则化算法的NARX(NARX—BR)神经网络方法和NARX—IR神经网络方法进行性能评价。结果表明:BP—LM神经网络方法难以描述接触线表面不平顺与弓网接触力的复杂关联关系;不论在训练还是预测中,NARX—IR神经网络方法的根均方误差均小于NARX—BR神经网络方法,而相关系数则大于NARX—BR神经网络方法。由此可推断:NARX—IR神经网络方法更适合于分析接触线表面不平顺与弓网接触力的关联关系。For collecting the test data of catenary irregularities and contact forces, the pantograph/catenary dynamics model was established and dynamic simulation was conducted using MATLAB/Simulink. After normalization, the catenary irregularities data and contact forces data were used respectively as the inputs and outputs of Nonlinear Auto-Regressive with eXogenous Input (NARX) neural networks. The traditional Bayesian Regularization (BR) was improved to modify the weight of NARX neural network, and the improved NARX (NARX-IR) neural network method was obtained. With NARX-IR neural network method, the correlation analysis between catenary irregularities and pantograph-catenary contact forces was carried through. The BP neural network based on LM algorithm (BP-LM), the NARX neural network based on traditional Bayesian regularization algorithm (NARX-BR), and the NARX neural network based on improving traditional Bayesian regularization algorithm (NARX-IR), three methods were tested and compared, and root mean square error and correlation coefficient were used for performance evaluation. The result shows that it's difficult for BP-LM neural network method to describe the complicated correlation between catenary irregularities and pantograph-catenary contact forces. Whether in training or predicting, the root mean square error of NARX-IR neural network method is lower than that of NARX-BR, and its correlation coefficient is greater than that of NARX-BR. It is concluded that the proposed NARX-IR neural network method is more suitable for analyzing the correlation between catenary irregularities and pantograph-catenary contact forces.

关 键 词：接触线 表面不平顺 弓网接触力 神经网络 关联分析 

分 类 号：U225.3[交通运输工程—道路与铁道工程] TP183[自动化与计算机技术—控制理论与控制工程]