Self-learning algorithm optimization for the rolling force model of hot strips

The influences of the number of rolled strips,the quality of measured data and the tolerance of rolling force prediction were taken into account for building a self-learning speed optimization model of rolling force.The grades and values of thickness and width were considered in the determinant condition of long-term self-learning to reduce the frequency of size change.The information of equipment states which was separated from the data of the last strip was used into the calculation of long-term self-learning factor to improve the continuity of the self-learning model.Offline simulation results show that the accuracy of the rolling force model is improved after the self-learning algorithm is optimized.