Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction

被引：94

作者：

Wang, Xinzhe ^{[1
]}

Han, Min ^{[1
]}

Wang, Jun ^{[2
]}

机构：

[1] Dalian Univ Technol, Sch Elect & Informat Engn, Dalian 116023, Peoples R China

[2] Chinese Univ Hong Kong, Fac Engn, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2010年 / 23卷 / 06期

基金：

国家高技术研究发展计划(863计划);

关键词：

Basic oxygen furnace; Mutual information; Support vector machine; Variables selection;

D O I：

10.1016/j.engappai.2009.12.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

Basic oxygen furnace (BOF) steelmaking is a complex process and dynamic model is very important for endpoint control. It is usually difficult to build a precise BOF endpoint dynamic model because many input variables affect the endpoint carbon content and temperature. For this problem, two effective variables selection steps: mechanism analysis and mutual information calculation are proposed to choose appropriate input variables according to a variable selection algorithm. Then, the selected inputs are weighted on the basis of mutual information values. Finally, two input weighted support vector machine BOF endpoint dynamic models are constructed to predict endpoint carbon content and temperature. Results show that the variable selection for BOF endpoint prediction model is essential and effective. The complexity and precise of two endpoint prediction models are improved. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：1012 / 1018

页数：7

共 14 条

[1]

Adapting a mathematical model of the end of the blow of a converter heat to existing conditions in the oxygen-converter shop at the Magnitogorsk Metallurgical Combine [J].