Pantograph-catenary contact force provides the main basis for evaluation of current quality collection; however,the pantograph-catenary contact force is largely affected by the catenary irregularities.To analyze the c...Pantograph-catenary contact force provides the main basis for evaluation of current quality collection; however,the pantograph-catenary contact force is largely affected by the catenary irregularities.To analyze the correlated relationship between catenary irregularities and pantograph-catenary contact force,a method based on nonlinear auto-regressive with exogenous input(NARX) neural networks was developed.First,to collect the test data of catenary irregularities and contact force,the pantograph/catenary dynamics model was established and dynamic simulation was conducted using MATLAB/Simulink.Second,catenary irregularities were used as the input to NARX neural network and the contact force was determined as output of the NARX neural network,in which the neural network was trained by an improved training mechanism based on the regularization algorithm.The simulation results show that the testing error and correlation coefficient are 0.1100 and 0.8029,respectively,and the prediction accuracy is satisfactory.And the comparisons with other algorithms indicate the validity and superiority of the proposed approach.展开更多
基金Project(20120009110035)supported by Specialized Research Fund for the Doctoral Program of Higher Education of ChinaProject(2011BAG01B05)supported by National Key Technology Research and Development Program of ChinaProject(2011AA110501)supported by National High-tech Research and Development Program of China
文摘Pantograph-catenary contact force provides the main basis for evaluation of current quality collection; however,the pantograph-catenary contact force is largely affected by the catenary irregularities.To analyze the correlated relationship between catenary irregularities and pantograph-catenary contact force,a method based on nonlinear auto-regressive with exogenous input(NARX) neural networks was developed.First,to collect the test data of catenary irregularities and contact force,the pantograph/catenary dynamics model was established and dynamic simulation was conducted using MATLAB/Simulink.Second,catenary irregularities were used as the input to NARX neural network and the contact force was determined as output of the NARX neural network,in which the neural network was trained by an improved training mechanism based on the regularization algorithm.The simulation results show that the testing error and correlation coefficient are 0.1100 and 0.8029,respectively,and the prediction accuracy is satisfactory.And the comparisons with other algorithms indicate the validity and superiority of the proposed approach.
