Weighted Prediction Model of Hot Rolled Strip Crown Based on Random Forest and Support Vector Machine
In view of low prediction accuracy and slow speed of traditional prediction methods for strip crown,a weighted prediction model based on random forest(RF)and support vector machine(SVM)was established.The parameters of models based on RF,SVM,and a combination of RF and SVM were optimized respectively by adopting the improved coati optimization algorithm(ICOA),so as to improve crown prediction accuracy.A 1580 mm production line of a hot-rolling mill in one company was taken in a simulation research on crown prediction based on its actual measurement.The root mean square error of the weighted prediction model based on RF and SVM is 2.23 μm.It is found that this weighted prediction model has its prediction accuracy increased by 7.08%and 2.62%respectively,compared with the models based on RF and SVM respectively.
crown predictionhot rolling stripsupport vector machine(SVM)coati optimization algorithm(COA)crownrandom forest(RF)
万方数据
维普
