摘要
为了克服露天矿边坡变形预测时传统算法精度低的问题,利用改进灰狼算法(IGWO)和支持向量机(SVM)建立了露天矿边坡变形IGWO-SVM模型。引入非线性递减的收敛因子策略和惯性权重策略改进灰狼优化算法,用来确定SVM参数,以达到提高模型精度的目的,并将露天矿边坡变形观测数据输入模型进行验证。结果表明,与SVM和BP模型相比,IGWO-SVM模型绝对误差最大值6.16 mm、最小值0.34 mm,相对误差平均值2.17%,说明IGWO-SVM模型预测精度高、综合性能好,证实该模型用于露天矿边坡变形预测是可行的。
To realize the slope deformation prediction for open-pit mines and overcome the shortcomings of low prediction accuracy of traditional algorithms,an IGWO-SVM model was established for the slope deformation of open-pit mine by adopting an improved gray wolf optimizer(IGWO)and support vector machine(SVM).The grey wolf optimizer was firstly improved by introducing non-linear decreasing convergence factor strategy and inertia weight strategy to determine the SVM parameters,so as to improve the accuracy of the model.Then,the observed slope deformation data of open-pit mine were input into the model for verification.The results show that compared with the SVM and BP models,the IGWO-SVM model has the absolute error maximally at 6.16 mm,minimally at 0.34 mm,and a mean relative error of 2.17%,indicating that the IGWO-SVM model can provide high prediction accuracy and has a good comprehensive performance.It is proven that this model is feasible to be used for the deformation prediction of open-pit mine slopes.
作者
胡军
邱俊博
栾长庆
张瀚斗
HU Jun;QIU Jun-bo;LUAN Chang-qing;ZHANG Han-dou(School of Civil Engineering,University of Science and Technology,Anshan 114051,Liaoning,China;Mineral Processing Branch,Gongchangling Mining Co Ltd,Anshan Iron and Steel Group Co Ltd,Liaoyang 111008,Liaoning,China;Donganshan Sintering Plant,Anshan Iron and Steel Group Co Ltd,Anshan 114041,Liaoning,China)
出处
《矿冶工程》
CAS
CSCD
北大核心
2022年第1期15-18,共4页
Mining and Metallurgical Engineering
基金
辽宁省教育厅项目(2017LNZ003)
辽宁科技大学研究生科技创新项目(LKDYC201922)。
