进化算法优化GBDT的带钢卷取温度预测  

Optimizing GBDT’s Strip Coiling Temperature Prediction with the Evolutionary Algorithm

作　　者：皮理想 崔桂梅[1] PI Lixiang;CUI Guimei(College 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,包头014010

出　　处：《华南师范大学学报（自然科学版）》2022年第1期122-127,共6页Journal of South China Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(61763039)。

摘　　要：针对B钢厂2250 mm热轧生产线层流冷却系统卷取温度预报命中率低的问题,采用差分进化算法优化后的梯度提升决策树建立带钢卷取温度预测模型(DE-GBDT),并对DE-GBDT预测模型与3个基础预测模型(梯度提升决策树(GBDT)、支持向量机(SVM)、小波神经网络(WNN)预测模型)以及差分进化算法优化后的支持向量机(DE-SVM)、小波神经网络(DE-WNN)进行对比。实验结果显示DE-GBDT预测模型能为提高带钢卷取温度控制精度提供有力支持:(1)与DE-SVM、DE-WNN预测模型相比,DE-GBDT预测模型的各项误差指标均最小,其中均方误差为18.232。(2)DE-GBDT预测模型的各项误差指标明显小于3个基础预测模型,其中,与GBDT预测模型相比,DE-GBDT预测模型的预测命中率提高了2.9%,均方误差降低了40.294,说明差分进化算法能明显提升模型性能。Considering the low hit rate of the coiling temperature prediction of the laminar cooling system of the 2250 mm hot rolling production line in Steelworks B,the gradient boosting decision tree optimized with the diffe-rential evolution algorithm(DE-GBDT)is used to establish the strip coiling temperature prediction model.Five regression prediction models,including Support Vector Machine(DE-SVM)and Wavelet Neural Network(DE-WNN)optimized with the differential evolution algorithm and the three basic prediction models(Gradient Boosting Decision Tree(GBDT),Support Vector Machine(SVM)and Wavelet Neural Network(WNN)),are added to the experiment for comparison.The experimental results show that DE-GBDT prediction model can provide strong su-pport for improving the precision of strip coiling temperature control:(1)compared with DE-SVM and DE-WNN,the DE-GBDT prediction model has the smallest error indicators and the mean square error is 18.232;(2)compared with the three basic prediction models,the error indicators of the DE-GBDT prediction model are significantly smaller than those of the three basic prediction models;compared with the GBDT prediction model,the hit rate of the DE-GBDT prediction model has increased by 2.9%and the mean square error has been reduced by 40.294,indicating that the differential evolution algorithm can significantly improve the model performance.

关 键 词：层流冷却 卷取温度预测 差分进化算法 梯度提升决策树 支持向量机 小波神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]