Prediction of sinter burn-through point based on support vector machines

被引：0

作者：

Wu, Xiaofeng ^{[1
]}

Fei, Minrui

Wang, Heshou

Zheng, Shuibo

机构：

[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China

[2] Laiwu Steel Grp, Dept Automat, Laiwu 271104, Peoples R China

[3] Shanghai Fire Res Inst, Minist Publ Secur, Shanghai 200032, Peoples R China

来源：

INTELLIGENT CONTROL AND AUTOMATION | 2006年 / 344卷

关键词：

D O I：

暂无

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 order to overcome the long time delays and dynamic complexity in industrial sintering process, a modeling method of prediction of burn-through point (BTP) was proposed based on support vector machines (SVMs). The results indicate SVMs outperform the three-layer Backpropagation (BP) neural network in predicting burn-through point with better generalization performance, and are satisfactory. The model can be used as plant model for the burn-through point control of on-strand sinter machines.

引用

页码：722 / 730

页数：9

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