Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference

Optimize BP Neural Network Structure on Car Sales Forecasts Based on Genetic Algorithm

Authors
Jun Tang, Qing Wu
Corresponding Author
Jun Tang
Available Online March 2015.
DOI
10.2991/iiicec-15.2015.18How to use a DOI?
Keywords
prediction; (BP) neural network; linear correlation; genetic algorithm
Abstract

In order to improve the ability of BP neural network to fit complex functions, we improve the structure of the BP neural network and optimize the weights and thresholds of structure of the BP neural network based on genetic algorithm, then, training the BP neural network model to improve its capability, so, we can apply the model to the automobile sales forecasting system. We compare the prediction accuracy with the traditional BP neural algorithm, it shows that this method obviously fits the data better and has higher prediction accuracy to dates with significant linear correlation.

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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Volume Title
Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
Series
Advances in Computer Science Research
Publication Date
March 2015
ISBN
978-94-62520-54-7
ISSN
2352-538X
DOI
10.2991/iiicec-15.2015.18How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Jun Tang
AU  - Qing Wu
PY  - 2015/03
DA  - 2015/03
TI  - Optimize BP Neural Network Structure on Car Sales Forecasts Based on Genetic Algorithm
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 75
EP  - 79
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.18
DO  - 10.2991/iiicec-15.2015.18
ID  - Tang2015/03
ER  -