Prediction of Silicon Content in Hot Metal Based on RS-LSSVM Model

摘要：

A model for prediction of silicon content in hot metal is proposed based on two integrated algorithms: attribute reduction algorithm of rough sets (RS) and least square support vector machine (LSSVM). Rough sets theory is used to construct decision table, discrete attributes, rank the importance of attributes and reduce attributes based on weighting-coefficient cumulative estimation. The key factors are extracted as the input variables of LSSVM. The method can reduce the dimensions of the data and the complexity, and improve the efficiency of training and the accuracy of prediction. The data of the model are collected from No.6 Blast Furnace in Baotou Iron and Steel Group Co. of China. The results show that the LSSVM model based on RS attribute reduction has better prediction results than the model using other variables. The hit rate of silicon content in hot metal reaches 90％ at the range of ± 0.1 ％ based on the proposed model, which can meet the requirement of practical production.

关键词： Rough Sets Least Square Support Vector Machine Silicon Content in Hot Metal Prediction

作者: Yikang Wang Min Zhao

作者单位: College of Science, China Jiliang University, Hangzhou, 310018,China Department of Mathematics, Zhej Department of Mathematics, Zhejiang University, Hangzhou, 310027, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 3486-3491

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）