Research on Equipment Materials Demand Forecast based on Genetic BP-Neural Networks

DOI

10.2991/iiicec-15.2015.108How to use a DOI?

Keywords

equipment materials support; BP-neural networks; genetic algorithm

Abstract

Accurate demand forecasting is an important precondition to carry out an active and detailed oriented equipment materials support. Learning and self-adaptive ability of BP-neural networks is used to learn the law of equipment demand, with genetic algorithm combined to improve the convergence speed of BP-neural networks. An optimized algorithm combining BP-neural networks and genetic algorithm is proposed for forecasting equipment materials demand. The simulation result shows that the proposed method owns high precision and fast convergence compared with original BP-neural networks.

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  - Tiening Wang
AU  - Longtao Wu
PY  - 2015/03
DA  - 2015/03
TI  - Research on Equipment Materials Demand Forecast based on Genetic BP-Neural Networks
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 470
EP  - 473
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.108
DO  - 10.2991/iiicec-15.2015.108
ID  - Wang2015/03
ER  -