Abstract: In view of the drawbacks of the traditional bending force preset model and the characteristics of hot strip rolling， a bending force prediction model is built based on genetic algorithm(GA)and artificial neural network(ANN)and the prediction is carried out on the end stand of finishing mill using a large amount of production data from 1580mm hot rolling line in a steel corp. The features of the model are as follows: a large number of actual data is used as input for the ANN training， the influence of various input parameters are fully considered， the framework of the model is relatively simple and easy to implement， and the overall performance is evaluated by the mean absolute percentage error， root mean square error and correlation coefficient. By comparing the predicted results with the experimental ones， the prediction accuracy of the model is verified. It shows that the GA-ANN prediction model of roll bending force can realize a high nonlinear fitting， which is suitable for improving the accuracy to control head shape of hot strip rolling. This study provides guidance and test foundation for the actual bending force setting.

Key words: artificial neural network, shape of hot strip rolling, production data, bending force, genetic algorithm