Study On Locomotive Traction Characteristic Test System

Under the circumstances of putting new railway lines into use or improving passenger train speed or increasing freight train tonnage or reforming driving system, locomotive tractive characteristic test has become a foundational technical job. It provides scientific data for railway administration to make management decision and transportations plan, to dig potential and increase profit. The real time calculation of tractive force and braking force is the core of the system. Aiming at the disadvantages of traditional calculations of locomotive tractive and braking force,the method of Artificial Neural Network model is proposed. The structure design of the neural network iselaborated.The paper finds out the disadvantages of traditional method. At the meantime,by researching theory and characteristic of the Artificial Neural Network and analyzing its nonlinear approaching capacity,the writer probes into the feasibility of the application of Artificial Neural Network applying to the locomotive tractive characteristic test.A calculation of tractive force and braking force model using Error Back-Propagation Training neural network(BP netwok) is proposed. The structure design of the neural network iselaborated.the problems such as choice about the node number of hidden layer,data pretreatment ,and so on are dicussed;in allusion to the shortages of BP arithmetic such as easy to fall into local smallness and slow constringency speed,the paper brought forward the amelioratin measures.In the end, the simulation is carried out with Matlab 6.5 using the data provided by Chengdu Railway Bureau and these results are presented. It was shown that the accuracy of the result is increased.