Binary Particle Swarm Optimization for variable selection in Partial Least Squares Regression with application to Production Quality Modeling

摘要：

In order to build the regression model between production process parameters and quality results of products in cold rolling strip hot-dip galvanizing, particle swarm optimization algorithm is used to perform variable selection in partial least squares to determine which some variables were be chosen. The cold rolling strip hot-dip galvanizing data consists of nine components of active substances, optimization variables selection solutions are able to improve the regression model which can eliminate some unimportant or uninformative variables and obtain the more simple and optimal prediction model. Because the obtained model contains few variables, it is easy to analyse or explains the variables effect to the model. Hence the method can be used to discriminate or determine the production process parameters effectively.

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: 117-120

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）