Quality Monitoring Method of Strip Hot-dip Galvanizing based on Partial Least Squares Regression and Least Square Support Vector Machine

摘要：

The partial least squares regression method is applied to analyze the process control parameters affecting the production quality of strip hot-dip galvanizing to extract the most important components. So that the problem of multiple correlations can be solved and the number of input dimensions of least square support vector machine can be reduced to avoid the nonlinear problem happened to the application of least square support vector machine. A quality monitoring method for strip hot-dip galvanizing based on the combination of partial least squares regression with partial least square support vector machine is proposed. The iron and steel enterprise application example shows that this model has higher precision and higher training efficiency than the models based on partial least squares regression or partial least square support vectors machine alone.

关键词： Strip hot-dip galvanizing Partial least squares regression Least square support vector machine,Quality monitoring method

作者: Yang Bin Zhang Lijun He Fei

作者单位: Scientific Center for Material Service Safety, University of Science and Technology Beijing, Beijing Mechanical Engineering School, University of Science and Technology Beijing, Beijing 100083, China

会议类型: 国际会议

会议名称: The 6th International Conference on Partial Least Squares and Related Methods(第六届偏最小二乘及相关方法国际会议)

会议地点: 北京

会议语种:英文

页码: 320-323

在线出版日期: 2009-09-04（万方平台首次上网日期，不代表论文的发表时间）