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.