基于PSO-MK-ELM的动车轮对尺寸预测模型

动车轮对安全尺寸预测为动车安全性评估提供了依据。由于轮对尺寸变化受到运行环境等因素影响的复杂性,提出了一种适用于动车轮对尺寸数据的粒子群优化多核极限学习机(PSO-MK-ELM)预测模型。将多项式核函数和径向基核函数加权构成的多核函数(MK)引入极限学习机中,并采用粒子群优化算法对模型的4个关键参数进行寻优。针对CRH2车型的动车车轮直径数据,通过对比不同算法的预测结果,验证该方法的合理性和准确性。预测结果表明,在动车轮对尺寸数据的预测上,PSO-MK-ELM预测模型能够获取比BP模型、ELM模型和3种常用KELM模型更好的拟合优度、均方差、平均绝对误差和平均绝对百分比误差,验证了模型在动车轮对尺寸预测上的有效性。

Prediction model of EMU wheel set size based on PSO-MK-ELM

The prediction of safety size of EMU wheel set provides a basis for EMU safety evaluation.Due to the complexity of wheel set size change affected by operating environment and other factors, this paper proposed a particle swarm optimization multi-kernel extreme learning machine (PSO-MK-ELM) prediction model which was suitable for wheel set size data.The multi-kernel (MK)function, which was composed of polynomial kernel function and radial basis kernel function, was introduced into the extreme learning machine, and four key parameters of the model were optimized by particle swarm optimization.According to the wheel diameter data of CRH2 model, the rationality and accuracy of this method were verified by comparing the prediction results of different algorithms.The prediction results show that PSO-MK-ELM model can obtain better goodness of fit, mean square deviation, mean absolute error and mean absolute percentage error than BP model, ELM model and three kinds of commonly used KELM model in the prediction of EMU wheelset size data, which verifies the validity of the model in the prediction of EMU wheelset size.