摘要 Aiming at the problem of insufficient prediction accuracy of strip flatness at the outlet of cold tandem rolling, the prediction performance of strip flatness based on different ensemble methods was studied and a high-precision prediction ensemble model of strip flatness at the outlet was established. Firstly, based on linear regression (LR), K nearest neighbors (KNN), support vector regression, regression trees (RT), and backpropagation neural network (BPN), bagging, boosting, and stacking ensemble methods were used for ensemble experiments. Secondly, three existing ensemble models, i.e., random forest, extreme random tree (ET) and extreme gradient boosting, were used to conduct experiments and compare the results. The research shows that bagging, boosting, and stacking three ensemble methods have the most significant improvement in the prediction accuracy of the regression trees model, which is increased by 5.28%, 6.51%, and 5.32%, respectively. At the same time, the stacking ensemble method improves both the simple model and the complex model, and the improvement effect on the simple base model is the greatest, which is 4.69% higher than that of the base model KNN. Comparing all of the ensemble models, the stacking ensemble model of level-1 (ET, AdaBoost-RT, LR, BPN) paired with level-2 (LR) was discovered to be the best model (EALB-LR) and can be further studied for industrial applications.
Abstract：Aiming at the problem of insufficient prediction accuracy of strip flatness at the outlet of cold tandem rolling, the prediction performance of strip flatness based on different ensemble methods was studied and a high-precision prediction ensemble model of strip flatness at the outlet was established. Firstly, based on linear regression (LR), K nearest neighbors (KNN), support vector regression, regression trees (RT), and backpropagation neural network (BPN), bagging, boosting, and stacking ensemble methods were used for ensemble experiments. Secondly, three existing ensemble models, i.e., random forest, extreme random tree (ET) and extreme gradient boosting, were used to conduct experiments and compare the results. The research shows that bagging, boosting, and stacking three ensemble methods have the most significant improvement in the prediction accuracy of the regression trees model, which is increased by 5.28%, 6.51%, and 5.32%, respectively. At the same time, the stacking ensemble method improves both the simple model and the complex model, and the improvement effect on the simple base model is the greatest, which is 4.69% higher than that of the base model KNN. Comparing all of the ensemble models, the stacking ensemble model of level-1 (ET, AdaBoost-RT, LR, BPN) paired with level-2 (LR) was discovered to be the best model (EALB-LR) and can be further studied for industrial applications.
Wu-quan Yang,Zhi-ting Zhao,Liang-yu Zhu, et al. Strip flatness prediction of cold rolling based on ensemble methods[J]. Journal of Iron and Steel Research International, 2024, 31(1): 237-251.
2024, Vol. 31 
