Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

Prediction Method of Railway Freight Volume Based on Improved Neural Network

Authors
Zhida Guo, Jingyuan Fu
Corresponding Author
Zhida Guo
Available Online September 2016.
DOI
https://doi.org/10.2991/icence-16.2016.174How to use a DOI?
Keywords
Railway Freight Volume; Forecasting; General Regression Neural Network; Genetic Algorithm
Abstract

Railway freight transportation is an important part of national economy. Accurate forecast of railway freight volume is significant to the planning, construction, operation and decision-making of railways. After analyzing the application status of general regression neural network (GRNN) in prediction method of railway freight volume, this paper improves the performance of this model by using improved neural network. In the improved method, genetic algorithm (GA) is adopted to search the optimal spread which is the only factor of GRNN, and then the optimal spread is used for forecasting in GRNN. Finally, the railway freight volumes in the example are forecasted based on this method.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
Series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-229-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icence-16.2016.174How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhida Guo
AU  - Jingyuan Fu
PY  - 2016/09
DA  - 2016/09
TI  - Prediction Method of Railway Freight Volume Based on Improved Neural Network
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 936
EP  - 940
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.174
DO  - https://doi.org/10.2991/icence-16.2016.174
ID  - Guo2016/09
ER  -