Prediction of Rolling Force Based on Artificial Bee Colony Algorithm and Back Propagation Neural Network in Aluminum Hot Tandem Rolling
ZHAO Zhi-wei1,2,3, YANG Jing-ming1,2, CHE Hai-jun1,2, HU Zi-yu1, WANG Xi1
1.Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004,China;
2.National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Qinhuangdao, Hebei 066004, China;
3.Department of Computer Science and Technology, Tangshan College, Tangshan, Hebei 063000, China
Abstract After analysing the traditional mathematic model of rolling force, a prediction method of rolling force based on the artificial bee colony Algorithm and back propagation neural network is presented, which is used in aluminum hot tandem rolling. The initial weights and threshold values of back propagation neural network are optimized by artificial bee colony algorithm. The training and testing samples are collected from the finishing mills. Comparing with the Sims mathematical model and back propagation neural network, the experimental results show that the prediction accuracy and error of rolling force is superior to that of the traditional methods.
ZHAO Zhi-wei,YANG Jing-ming,CHE Hai-jun, et al. Prediction of Rolling Force Based on Artificial Bee Colony Algorithm and Back Propagation Neural Network in Aluminum Hot Tandem Rolling[J]. Acta Metrologica Sinica, 2014, 35(2): 157-160.
[1] Orowan E. The calculation of roll pressure in hot and cold flat rolling[J]. Proceedings of the Institution of Mechanical Engineers, 1943, 150(1): 140-167.
[2] Sims R B. The calculation of roll force and torque in hot rolling mills[J]. Proceedings of the Institution of Mechanical Engineers, 1954, 168(1): 191-200.
[3] Alexander J M, Ford H. Simplified hot-rolling calculations[J]. J INST MET, 1964, 92: 397-404.
[4] Portmann N F, Lindhoff D, Sorgel G, et al.Application of neural networks in rolling mill automation[J]. Iron Steel Eng, 1995, 72(2): 33-36.
[5] Lee D, Lee Y. Application of neural-network for improving accuracy of roll-force model in hot-rolling mill[J].Control Engineering Practice,2002, 10(4): 473-478.
[6] Lee D M, Choi S G. Application of on-line adaptable Neural Network for the rolling force set-up of a plate mill[J]. Engineering applications of artificial intelligence, 2004, 17(5): 557-565.
[7] 苏彩红, 向娜, 陈广义, 等. 基于人工蜂群算法与BP神经网络的水质评价模型[J]. 环境工程学报, 2012, 6(2): 699-704.
[8] 罗钧, 吴华, 王强. 具有快速收敛特性蜂群算法的球度误差评定[J]. 计量学报, 2011, 32(6): 501-504.
[9] 冯楠, 王振臣, 胖莹. 基于自适应遗传算法和 BP 神经网络的电池容量预测[J]. 计量学报, 2012, 33(6): 546-549.
[10] 张媛媛, 徐科军, 许耀华, 等. PSO 算法结合 BP 神经网络在传感器静态非线性校正中的应用[J]. 计量学报, 2009, 30(6): 526-529.
[11] Hagan M T, Menhaj M B. Training feedforward networks with the Marquardt algorithm[J]. Neural Networks, IEEE Transactions on, 1994, 5(6): 989-993.
2014, Vol. 35 
