RBF神经网络在铁路客运成本预测中的应用

铁路运输客运成本指的是铁路运输企业为了完成客运运输作业在运输过程中所耗费的一切费用的支出,包括运输生产过程中生产资料的消耗和劳动力的消耗。合理地控制铁路运输成本可以有效地提高铁路运输企业的管理水平、经营状况等。可见,选取符合具有铁路成本特点的预测方法准确地对运输成本进行预测具有重要的意义。通过对铁路客运成本的影响因素进行分析,选取主要影响因素并结合RBF神经网络超强的学习能力和适应能力建立铁路客运成本预测模型进行预测。最后,通过案例分析得到RBF神经网络对客运量成本具有很好的预测性。

Study on the Prediction of Railway Passenger Transport Cost Based on RBF Neural Network

Railway passenger transport cost refers to all the expenditures paid by the railwaytransport enterprise to complete the operations in the process of transportation,including the consumed means of production and labor. Reasonable cost control of railway transportation can effectively improve the management level of the railway transport enterprise and operating efficiency. Selecting appropriate prediction method plays an important role in forecasting the cost of transportation Therefore,this paper analyzes the influence factors of railway passenger transport cost,selects main influencing factors and employs RBF neural network with strong learning ability and adaptability to establish railway passenger transport cost prediction model. Finally,through case analysis,RBF neural network is proved effective in predicting passenger traffic cost.