Driving curve algorithm for heavy haul train based on improved BP Neural Network
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摘要: BP神经网络被用于重载列车驾驶曲线研究，利用列车实际驾驶数据进行神经网络学习，描述列车制动时的非线性特性，对列车制动运行过程建模，获得列车制动减压目标值及缓解时间进行运动方程运算，最终获得重载列车驾驶曲线。通过在朔黄线线路上由该模型仿真得到的驾驶曲线和实际列车驾驶曲线比较，结果表明该方法研究驾驶曲线是有效的。Abstract: This article proposed driving curve algorithm for heavy haul train (HHT) based on improved BP Neural Network (NN), used the actual train driving data for NN learning, described the nonlinear characteristics when the train braked, set the NN model of train braking operation process. The decompression target value of train braking and release time were used for the calculation of motion equation to obtain the driving curve for HHT. By comparing the curve with the real data from the Shuo-Huang Heavy Haul Line, the results showed that the method was effective.
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Keywords:
- heavy haul train (HHT) /
- Neural Network (NN) /
- simulation and modeling
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