Deviation prediction of exit thickness of finished first rolled steel coil based on improved BP algorithm of genetic algorithm
In order to solve the widely existing problem of excessive head thickness deviation of the first strip after finishing roll change in hot strip mill,a hybrid feature selection method based on vari-ance selection,mutual information,L1/2 regularization combined with experts'experiences was pro-posed.The method is used to select features from historical production data of the first strip after roll change in a 1 700 hot strip mill in China,and the feature selection results are used as the training set of GA-BP neural network-based head thickness deviation prediction model for the first strip coil after finishing roll change.A series of experiments are carried out on the model,and mean absolute per-centage error(MAPE),mean square error(MSE),coefficient of determination(R2)and other indi-cators are used as model evaluation criteria.The results show that the hybrid feature selection method proposed in this paper has significantly improved the prediction accuracy of the model after training compared with the traditional mathematical feature selection method.Through testing on data samples of different steel grades and different thickness intervals of the main products of a steel mill,it is veri-fied that the model has a high prediction accuracy with a certain degree of generalizability.The meth-od has good application prospects in production practice.
万方数据
维普
