Prediction modeling of silicon content in liquid iron based on multiple kernel extreme learning machine and improved grey wolf optimizer

被引：0
|
作者：
Fang Y.-M. [1 ,2 ]
Zhao X.-D. [1 ]
Zhang P. [1 ]
Liu L. [1 ]
Wang S.-Y. [3 ]
机构：
[1] Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, Hebei
[2] Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, 066004, Hebei
[3] School of Systems Engineering, Kochi University of Technology, Kami
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Improved grey wolf optimizer; Multiple kernel extreme learning machine; Optimal-worst orthogonal opposition-based learning; Prediction modeling; Silicon content in liquid iron;
D O I：
10.7641/CTA.2020.90571
中图分类号：
学科分类号：
摘要：
Aiming at the difficulty of on-line detection of silicon content in liquid iron of blast furnace, a predictive modeling method of silicon content in liquid iron of blast furnace based on multiple kernel extreme learning machine (MKELM) optimized by improved grey wolf optimizer (IGWO) is proposed. Firstly, aiming at the shortage of the search ability of grey wolf optimizer (GWO), the optimal-worst orthogonal opposition-based learning (OWOOBL) is applied to the location update of grey wolf algorithm, and an IGWO is obtained. The simulation of 10 standard functions shows that the improved grey wolf optimizer has better optimization ability. Secondly, aiming at the insufficient regression ability of single kernel extreme learning machine (KELM), different kinds of kernel functions are weighted and combined, and the weighted coefficients and other parameters of the MKELM are optimized by IGWO. Finally, the prediction model of the silicon content in liquid iron of blast furnace is established based on the measured data of a steel plant. The simulation results show that the prediction effect of the proposed method is better than that of back propagation neural network (BP- NN), extreme learning machine (ELM), KELM andGWO-MKELM, and therefore the proposed method has a good guiding significance for blast furnace iron making. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：1644 / 1654
页数：10
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