The Temperature Prediction in Blast Furnace Base on Fuzzy Least Squares Support Vector Machine

被引：1
|
作者：
Zhang Qingxin [1 ]
Tao Yong [1 ]
Cui Zhanbo [1 ]
机构：
[1] Shenyang Aerosp Univ, Automat Dept, Shenyang, Peoples R China
来源：
INDUSTRIAL INSTRUMENTATION AND CONTROL SYSTEMS II, PTS 1-3 | 2013年 / 336-338卷
关键词：
temperature prediction; support vector data description; fuzzy membership; LS-FSVM; LSSVM;
D O I：
10.4028/www.scientific.net/AMM.336-338.566
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The temperature prediction in blast furnace loses accuracy or Forecasts failure when the temperature's change is at normal levels and obvious. This paper introduces fuzzy membership of samples basing on support vector data description and the fuzzy least squares support vector machine to forecast the blast furnace temperature. Then the simulation was done by using the forecast samples and the model after training by MATLAB. Comparing the simulation results of LS-FSVM with LS-SVM, the model basing on LS-FSVM enhances anti-jamming ability. The accuracy of the temperature prediction in blast furnace promotes significantly when the temperature of blast furnace fluctuates.
引用
收藏
页码：566 / 569
页数：4
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