Freight Volume Forecast of Wuhan City Circle Based on Wavelet Neural Network

DOI

10.2991/icmse-15.2015.63How to use a DOI?

Keywords

Wavelet neural network Freight volume forecast Morlet wavelet function Impact factor Comprehensive index

Abstract

Freight system is a complex system with multi factors coupling. The relationship among the factors of freight is staggered, showing strong randomness, uncertainty and nonlinearity. The wavelet neural network combines the self-learning ability of the neural network with the powerful detail feature of the wavelet, which has adaptive resolution and good tolerance.The freight volume of Wuhan city circle and eight important parameters which affect the freight volume taken as the basic data, the annual comprehensive index of the impact factor of the freight volume is obtained through the significant analysis of F statistics. Then a wavelet neural network is constructed with the enhanced flexibility by the introduction of translation factors and of the expansion factors. The parameters are optimized continuously until the whole network reaches the minimum error by training. So the network can extrapolate the future freight volumes. Through the test and analysis of the forecast results, the validity of the wavelet neural network used in the prediction of freight volume is proved.

Copyright

© 2015, 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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TY  - CONF
AU  - Zhengxiang Yang
AU  - Guimin Xu
PY  - 2015/12
DA  - 2015/12
TI  - Freight Volume Forecast of Wuhan City Circle Based on Wavelet Neural Network
BT  - Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering
PB  - Atlantis Press
SP  - 342
EP  - 348
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-15.2015.63
DO  - 10.2991/icmse-15.2015.63
ID  - Yang2015/12
ER  -