Railway Freight Turnover Forecast Based on the BP Neural Network

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 536-537)
• Pages: 837-840
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.536-537.837
• Online since: April 2014
• Authors: Jiang Sun, Chong Wei
• Keywords: Grey GM(1,1), Neural Network (NN), Railway Freight Turnover Forecast

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