Proceedings of the 2016 International Forum on Management, Education and Information Technology Application

Breakout prediction based on particle swarm optimization back propagation neural network in continuous casting process

Authors
Benguo Zhang, Xinjiang Zhang, Lifeng Fang
Corresponding Author
Benguo Zhang
Available Online January 2016.
DOI
https://doi.org/10.2991/ifmeita-16.2016.43How to use a DOI?
Keywords
Particle swarm optimization, BP neural network, Continuous casting, Breakout prediction
Abstract
Aiming at the two terrible drawbacks of slow convergence and local optimal solution in the training process of BP neural network, particle swarm optimization algorithm was introduced to the training process of the BP neural network to improve its converge property, so a PSO-BP neural network was established, and then it was introduced into the breakout prediction system. The PSO-BP breakout prediction neural network model was trained and tested with the historical data collected from a steel plant. The results show that the convergence rate of the PSO-BP neural network model is significantly improved comparing the traditional BP neural network, and the feasibility of the model is verified by the testing result with the accuracy rate of 96.39% and the prediction rate of 100%.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Forum on Management, Education and Information Technology Application
Part of series
Advances in Social Science, Education and Humanities Research
Publication Date
January 2016
ISBN
978-94-6252-166-7
ISSN
2352-5398
DOI
https://doi.org/10.2991/ifmeita-16.2016.43How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Benguo Zhang
AU  - Xinjiang Zhang
AU  - Lifeng Fang
PY  - 2016/01
DA  - 2016/01
TI  - Breakout prediction based on particle swarm optimization back propagation neural network in continuous casting process
BT  - 2016 International Forum on Management, Education and Information Technology Application
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/ifmeita-16.2016.43
DO  - https://doi.org/10.2991/ifmeita-16.2016.43
ID  - Zhang2016/01
ER  -