A BP neural network model was employed to forecast the railway freight turnover. First, this paper analyses the data of railway freight turnover in China from 1998 to 2012, build a three layers BP neural network, then by training and learning, a well-trained network can be used for simulating and forecasting. Finally, predict by the Grey GM(1,1) model and well-trained BP neural network respectively, and compares the errors of two prediction model, the results show that predicting the railway freight turnover by BP neural network has higher precision.
Railway Freight Turnover Forecast Based on the BP Neural Network
Applied Mechanics and Materials ; 536-537 ; 837-840
2014-04-04
4 pages
Article (Journal)
Electronic Resource
English
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