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

fYear

2012

fDate

11-13 Aug. 2012

Firstpage

2201

Lastpage

2204

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science & Service System (CSSS), 2012 International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4673-0721-5

Type

conf

DOI

10.1109/CSSS.2012.547

Filename

6394865

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2725134