Data-driven Modeling and Online Algorithm for Hot Rolling Process

摘要：

Based on the idea that the accuracy of model could be significantly improved by combing several sub-models,a multiple support vector machine (MSVM)modeling approach is proposed to build the strip thickness model in hot rolling Automatic Gauge Control (AGC)system.The subtractive clustering is adopted to divide the input space into several clusters,and each cluster subset is built by Least-square support vector machine.Then when the online data constantly increased,the clustering subset is updated on-line by subtractive clustering algorithm,and the parameter of each local model is updated by the recursion algorithm.The results of experiment demonstrate the method the effectiveness of the proposed modeling approach,and it has powerful ability of online learning.

作者: LIANG Hui TONG Chaonan PENG Kaixiang

作者单位: School of Information Engineering,University of Science and Technology Beijing 100083,P.R.China Key School of Information Engineering,University of Science and Technology Beijing 100083,P.R.Key Labora

会议类型: 国际会议

会议名称: The 30th Chinese Control Conference(第三十届中国控制会议)

会议地点: 烟台

会议语种:英文

页码: 1-5

在线出版日期: 2011-07-01（万方平台首次上网日期，不代表论文的发表时间）