The Prediction Model of Silicon Content in Hot Metal based on LS-SVR Optimized by Estimation Distributed Algorithm

摘要：

Accurate prediction of silicon content in hot metal is very helpful for operation of blast furnace. A prediction model of silicon content in hot metal based on least square support vector regression (LSSVR)is proposed in this paper. As the parameters of LS-SVR have great impact on prediction results, an estimation of distribution algorithm (EDA) is presented to optimize the parameters. The verifying result of practical data shows that the proposed algorithm can optimize LS-SVM parameters, which makes the prediction model has good efficiency.

关键词： least square support vector regression (LS-SVR) estimation of distribution algorithm (EDA) silicon content prediction

作者: Wang Gaopeng Wang Gaopeng

作者单位: National Iron & Steel Making Plant Integration Research Center, Chongqing 400013, P, R, China Automation Dept,,CISDI Engineering Co,, Ltd,,Chongqing 400013, P, R, China

会议类型: 国际会议

会议名称: 2011 6th Joint International Information Technology and Artificial Intelligence Conference(2011年第六届IEEE联合国际信息技术与人工智能会议 IEEE ITAIC 2011)

会议地点: 重庆

会议语种:英文

页码: 267-270

在线出版日期: 2011-08-20（万方平台首次上网日期，不代表论文的发表时间）