Breakout prediction for continuous casting using genetic algorithm-based back propagation neural network model

被引：15
|
作者：
Zhang, Benguo [1 ]
Zhang, Ruizhong [2 ]
Wang, Ge [1 ]
Sun, Lifeng [3 ]
Zhang, Zhike [2 ]
Li, Qiang [1 ,4 ]
机构：
[1] Yanshan Univ, Dept Mech Engn, Qinhuangdao 066004, Hebei, Peoples R China
[2] Handan Iron & Steel Co Ltd, CSP Plant, Handan 056015, Hebei, Peoples R China
[3] Hebei Univ Sci & Technol, Dept Polytech, Shijiazhuang 050018, Hebei, Peoples R China
[4] Hebei Univ Sci & Technol, Dept Mat Sci & Engn, Shijiazhuang 050018, Hebei, Peoples R China
来源：
INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL | 2012年 / 16卷 / 03期
关键词：
continuous casting; breakout prediction; genetic algorithms; GAs; BP neural network;
D O I：
10.1504/IJMIC.2012.047727
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the effectiveness of the genetic algorithm-based back propagation (GABP) neural network model and its application to the breakout prediction in the continuous casting process are investigated. The formation of the sticking-type breakouts and the prediction principle of thermocouple thermometry method are analysed firstly. Then the genetic algorithm-based back propagation neural network model is proposed by fusing genetic algorithm (GA), and error back propagation neural network to offset the demerits of one paradigm by the merits of another. Finally, the GABP neural network model is applied to the breakout prediction in the continuous casting process; and the feasibility of the model is verified by the testing result with the accuracy rate of 97.56% and the prediction rate of 100%.
引用
收藏
页码：199 / 205
页数：7
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