Title :
Application of BP Neural Network Forecast Model Based on Principal Component Analysis in Railways Freight Forecas
Author :
Jianguo, Zhou ; Gang, Qin
Author_Institution :
Sch. of Econ. & Manage., North China Electr. Power Univ., Baoding, China
Abstract :
This paper uses the BP neural network forecast model based on principal component analysis to predict China´s railways freight. It firstly regroups indexes affecting railways freight by principal component analysis as to make the dimensions of index reduced and unrelated, and then it makes use of BP neural network to built model, and predicts the railways freight. The forecast result indicates that the method this paper uses has high prediction accuracy.
Keywords :
backpropagation; forecasting theory; freight containers; principal component analysis; railway industry; BP neural network forecast model; China railways freight; high prediction accuracy; principal component analysis; railways freight forecas; Analytical models; Biological neural networks; Indexes; Predictive models; Principal component analysis; Rail transportation; Training; BP neural network; logistic; prediction; principal component analysis (PCA); railways freight;
Conference_Titel :
Computer Science & Service System (CSSS), 2012 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-0721-5
DOI :
10.1109/CSSS.2012.547
