Profile-Predicting for Hot-Rolled Steel Sheets Based on Neural Network Algorithms
The prediction model based on the Elman neural network was proposed and then the initial weight values and threshold values in terms of networks were optimized by syncretizing the Cuckoo Search (CS) algorithm so as to improve the prediction accuracy of the profiles of steel sheets. After collecting production data from Hot Rolled Strip Steel Mill of Angang Steel Co.,Ltd. and pre-processing these data,the model was exercised experimentally,the experimental results showed that the proposed CS-Elman model was characterized by having the mean absolute error (MAE) of 1.3693,mean square error(MSE) of 3.0843,mean absolute percentage error (MAPE) of 3.9025％,and coefficient of determination R2 of 0.95123. All these indicators showed signifi-cant improvement compared to the original Elman algorithm. This prediction model can effectively extract underlying laws from production data,which provided an effective solution for accurately predicting the profiles of steel sheets. So this model had significant practical application values for optimizing hot rolling processes and improving product quality.
万方数据
维普
