蚁群优化算法协同深度极限学习机的热连轧宽度预测模型  

Deep extreme learning machine based on ant colony optimization for hot strip mill width prediction model

作　　者：李嘉林 高杰 丁敬国[2] Li Jialin;Gao Jie;Ding Jingguo(Northeastern University School of Materials Science and Engineering,Shenyang 110819,China;Northeastern University State Key Laboratory of Rolling and Automation,Shenyang 110819,China)

机构地区：[1]东北大学材料科学与工程学院,沈阳110819 [2]东北大学轧制技术及连轧自动化国家重点实验室,沈阳110819

出　　处：《材料与冶金学报》2024年第5期497-504,共8页Journal of Materials and Metallurgy

基　　金：2022年辽宁省大学生创新创业训练计划项目(S202210145215);国家自然科学基金联合基金重点项目(U21A20475)。

摘　　要：热连轧粗轧生产过程中,为解决换规格后宽度设定精度低的难题,提出了一种蚁群优化算法协同深度极限学习机(ant colony optimization deep extreme learning machine,ACO-DELM)的热连轧粗轧宽度预测方法.该方法将蚁群优化算法应用于DELM网络中,以提高其预测精度和泛化能力.先利用数据预处理方法对原始数据进行异常值的剔除和数据归一化.然后,使用蚁群优化算法对DELM的隐藏层节点数、迭代次数进行优化,在隐藏层节点数达到95个、迭代次数为480次时,DELM模型的预测性能最佳,其在预测不同规格带钢平均宽度时,决定系数R^(2)达到0.9989,97.98%的样本点预测误差分布在-7~7 mm.应用结果表明,与传统的深度极限学习机(DELM)、卷积神经网络(CNN)等模型相比,ACO-DELM模型在预测精度和泛化能力上有明显的提升,可有效应用于热轧带钢的平均宽度预测.In order to solve the problem of low precision of width setting after changing specifications in the rough rolling process of the hot strip rolling mill,a rough rolling width prediction method based on the ant colony optimization algorithm and deep extreme learning machine(ACO-DELM)was proposed.The method applied the ant colony optimization algorithm to the DELM network to improve its prediction accuracy and generalization ability.Firstly,the original data were removed from the outliers,and the data were normalized by using data pre-processing methods.Then,the number of hidden layer nodes and the number of iterations of DELM were optimized by using the ACO algorithm.When the number of hidden layer nodes reached 95 and the number of iterations was 480,the prediction performance of the DELM model was the best.When predicting the average width of strip steel of different specifications,the determination coefficient R^(2) reached 0.9989,and the prediction error of 97.98%of the sample points was distributed in-7~7 mm.The application results show that compared with traditional deep extreme learning machine(DELM)and convolutional neural network model(CNN),the ACO-DELM model has obvious improvements in prediction accuracy and generalization ability,and can be effectively applied to the prediction of the average width of hot-rolled strip.

关 键 词：热连轧 蚁群优化算法 深度极限学习机 宽度预测 

分 类 号：TG335[金属学及工艺—金属压力加工] TP18[自动化与计算机技术—控制理论与控制工程]