基于改进BOA-ELM的热轧带钢宽度预测  

Prediction on hot rolled strip width based on improved BOA-ELM

作　　者：陈啸天 张帅 杨培宏[1] 张勇[1] Chen Xiaotian;Zhang Shuai;Yang Peihong;Zhang Yong(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China)

机构地区：[1]内蒙古科技大学信息工程学院,内蒙古包头014010

出　　处：《锻压技术》2024年第3期101-106,126,共7页Forging & Stamping Technology

基　　金：国家自然科学基金资助项目(62263026)。

摘　　要：针对传统粗轧宽度预测模型参数强耦合、非线性等特点,从数据驱动角度出发,提出一种基于改进蝴蝶算法优化极限学习机(IBOA-ELM)的粗轧宽度预测模型。首先,利用蝴蝶优化算法(BOA)对极限学习机(ELM)的随机权重和偏置进行参数寻优,以提高ELM模型的预测精度。然后,针对蝴蝶优化算法易陷入局部最优及收敛性差等问题,引入Fuch混沌映射、非线性惯性权重和折射反向学习等策略改进蝴蝶优化算法,进一步提高宽度预测模型的精度。最后,通过某钢厂热轧生产现场数据对该模型进行仿真测试。结果表明:基于数据驱动的IBOA-ELM模型在预测精度方面具有明显优势,预测粗轧宽度误差在±8 mm以内的命中率为93%,明显优于对照模型,可用于热轧带钢粗轧宽度预测且具有较强的适用性。For the characteristics of strong coupling and non-linearity of parameters in traditional rough rolling width prediction model,a new rough rolling width prediction model based on improved butterfly algorithm optimized extreme learning machine(IBOA-ELM)was proposed from the data-driven perspective.Firstly,the random weight and bias of the extreme learning machine(ELM)were optimized by butterfly optimization algorithm(BOA)to improve the prediction accuracy of ELM model.Then,for the problems that the butterfly optimization algorithm was easy to fall into local optimum and the convergence was poor,the butterfly optimization algorithm was improved by introducing the strategies of Fuch chaotic mapping,non-linearity inertia weights,refraction reverse learning and so on to further improve the accuracy of the width prediction model.Finally,the model was simulated and tested by the hot rolling production site data of a steel mill.The results show that the data-driven IBOA-ELM model has obvious advantages in prediction accuracy,and the hit rate of predicting the rough rolling width within±8 mm is 93%,which is significantly better than the comparison models,and can be used for predicting the rough rolling width of hot rolled strips with strong applicability.

关 键 词：粗轧宽度预测 热轧带钢 蝴蝶优化算法 Fuch混沌映射 非线性惯性权重 折射反向学习 

分 类 号：TG335.56[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]