Prediction of Railway Freight Volumes Based on AdaBoost_BP Neural Network

Aiming at the disadvantages of prediction model of single BP neural network, a prediction model was presented by combining AdaBoost algorithm and BP neural network for improving the forecasting accuracy of single BP neural network. A new updating method is proposed for the characters of ensemble BP neural network based on AdaBoost. The new method can update the model effectively and overcome the disadvantage of traditional updating methods. The efficiency of the proposed prediction model was tested by simulation of the railway freight volume statistical data from the 1999 to 2009 years in China. The simulation results have shown that the higher accuracy is expressed in this proposed model, and it is applicable to practice.