果蝇优化神经网络在冷轧弯辊模型中的应用  

Application of fruit fly optimization neural network in cold rolling bending model

作　　者：宋君 王奎越 曹忠华 SONG Jun;WANG Kuiyue;CAO Zhonghua(Ansteel Beijing Research Institute Co.,Ltd.,Beijing 102211,China)

机构地区：[1]鞍钢集团北京研究院有限公司,北京102211

出　　处：《冶金自动化》2023年第3期116-125,共10页Metallurgical Industry Automation

基　　金：国家重点研发计划项目(2017YFB0304100)。

摘　　要：为了解决冷连轧机弯辊力设定模型在工业生产中存在设定与控制精度低的问题,提出了一种基于果蝇优化算法(fruit fly optimization algorithm,FOA)优化广义回归神经网络(generalized regression neural network,GRNN)的混合弯辊力预测模型。采用FOA算法对GRNN网络参数光滑因子进行优化选择以保证模型最佳性能。将本文提出的混合FOA-GRNN冷连轧弯辊力预测模型和对应的后向传播神经网络(back propagation neural network,BPNN)预测模型进行对比分析,同时选择误差指标来对两种模型的综合性能进行评价,证明所提出的混合FOA-GRNN模型相对于BPNN模型能够更好地实现对冷连轧带钢弯辊力的精准预测。In order to solve the problem of low set and control precision of traditional roll bending force setting model for tandem cold rolling in production practice,a hybrid roll bending force prediction model based on fruit fly optimization algorithm(FOA)and generalized regression neural network(GRNN)was proposed.The smoothing factor parameters of GRNN network is optimized by FOA algorithm to ensure the best performance of the model.The hybrid FOA-GRNN roll bending force prediction model for tandem cold rolling in this paper was compared with the corresponding back propagation neural network(BPNN)prediction model.The comprehensive performance of the two models were evaluated by error indexes.It is proved that the hybrid FOA-GRNN model can better predict the roll bending force compared with BPNN model in tandem cold rolling.

关 键 词：冷轧带钢 数据驱动模型 神经网络 果蝇算法 弯辊力设定 

分 类 号：TG335.12[金属学及工艺—金属压力加工] TP183[自动化与计算机技术—控制理论与控制工程]