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

DOI

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%.

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/).

Download article (PDF)

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  - Proceedings of the 2016 International Forum on Management, Education and Information Technology Application
PB  - Atlantis Press
SP  - 230
EP  - 234
SN  - 2352-5398
UR  - https://doi.org/10.2991/ifmeita-16.2016.43
DO  - 10.2991/ifmeita-16.2016.43
ID  - Zhang2016/01
ER  -