Thickness Prediction of Zinc Layer of Hot-Dip Galvanized Sheet in Strip Based on Mic and Ipso-Rerm
Aiming at the problem that the thickness deviation of zinc layer of hot-dip galvanized strip sheet is affected by factors such as multi-variable strong coupling of production line and long lag time of thickness gauge,a thickness prediction method of zinc strip layer based on maximum information coefficient(MIC)and improved particle swarm algorithm optimization regularized extreme learning machine(IPSO-RELM)was proposed.Firstly,the production process data was collected for relevant preprocessing.Then,the MIC method was used to rank the importance of each parameter variable to determine the key factors affecting the thickness of the zinc layer.Finally,the RELM prediction model was established by taking the filtered variables as inputs,and the randomness parameters of the model were optimized by the IPSO algorithm,which effectively improved the stability and prediction accuracy of the model.The results show that the fitting coefficient of determination R2 of the predicted results of the established model is 94.66％,the hit rate of the sample points with the prediction error of-4-4 g/m2 reaches 96％,and the three evaluation indicators of the model are better than other comparison algorithms,which proves that the proposed method has high prediction accuracy and can lay a foundation for the improvement of the quality of strip hot-dip galvanized sheet.
万方数据
维普
