基于灰色遗传BP神经网络的铁路货运量预测

铁路货运量受限于社会建设、投资和政策调控等多方面的影响.为了给货运系统提供更好的调配支撑,基于铁路货运量影响因素,加强BP预测性能,提出灰色遗传BP神经网络的铁路货运量预测模型(GR_GA_BP),对铁路货运量进行灰色关联分析,找出与铁路货运量关联程度较高的指标作为GR_GA_BP预测模型的输入对货运量进行预测.通过MAPE、RMSE和MAE对比GR_GA_BP模型与其他模型,实验结果表明,GR_GA_BP模型优于其他模型,可以作为新方法预测和研究铁路货运能力.

Forecasting of Railway Freight Volume Based on Grey Genetic BP Neural Network

Railway freight volume is limited by the influence of social construction, investment and policy regulation. In order to provide better deployment support for the freight system, based on the influence factors of railway freight volume and strengthening BP prediction performance, the gray genetic BP is proposed Neural network railway freight volume prediction model (GR_GA_BP). The grey correlation analysis of railway freight volume is used to find out the index with higher correlation with railway freight volume, which is used as GR_ GA_ The input of BP prediction model predicts the freight volume.By comparing the GR_GA_BP model with other models through MAPE, RMSE and MAE, the experimental results show that the GR_GA_BP model is superior to other models and can be used as a new method to predict and study railway freight capacity.