Abstract：To improve the precision and efficiency of rolling force prediction on tandem cold rolling mill, a neural network model combined with ant colony algorithm is presented. The BP (Back Propagation) neural network model for rolling force prediction on tandem cold rolling mill was established according to rolling theory. Taking neural network weights and threshold values as decision variables, and neural network prediction error as objective function, the global minimum prediction error could be gotten through multiple generation computation of ant colony. Then training can be done by inputing the corresponding weights and threshold values to the neural network. Using field data on 1450 tandem cold rolling mill, the offline computation result showed that this method is capable of preventing local minimum of BP neural network, and has fast constringency. So it can be generalized in practice as a new method for rolling force prediction.
杨景明;孙晓娜;车海军;刘畅. 基于蚁群算法的神经网络冷连轧机轧制力预报[J]. 钢铁, 2009, 44(3): 52-0.
YANG Jingming;SUN Xiaona;CHE Haijun;LIU Chang. Neural Network Based on Ant Colony Algorithm for Rolling Force Prediction on Tandem Cold Rolling Mill. Iron and Steel, 2009, 44(3): 52-0.
2009, Vol. 44 
