摘要
针对小样本数据条件下预测硫化铜精矿品位的问题,提出了基于灰色理论的铜精矿品位预测模型。该模型通过试验所得的小样本数据,动态改变建模数据的初始值和背景值,结合灰色理论建立了硫化铜精矿品位的GM(1,1)预测模型,统计预测模型的平均相对误差。结果表明:基于灰色理论的预测模型精度较高,最小的平均相对误差为1. 88%,模型的预测效果较好,可作为预测铜精矿品位的一种新方式。
The paper was inclined to present the prediction model for concentrate grade of copper sulfide by using grey theory,in the view of prediction of concentrate grade under the condition of the small sample data. The said GM( 1,1) model,by utilizing the grey theory analysis of the small sample data,and being proposed by dynamic background value and initial value,was established to perform the mean relative error analysis. The results showed that the prediction mode based on grey theory could have high prediction accuracy,the mean relative errors reached 1. 88%,and the prediction effect was good. It is concluded that the model could provide a new way for predicting concentrate grade.
作者
任传成
韩金姝
杨建国
王广超
REN Chuancheng;HAN Jinshu;YANG Jianguo;WANG Guangchao(School of Information Management,Dezhou University,Dezhou Shandong 253023,China;National Engineering Research Center of Coal Preparation and Purification,China University of Mining and Technology,Xuzhou Jiangsu 221116,China)
出处
《有色金属（选矿部分）》
CAS
北大核心
2019年第1期39-42,共4页
Nonferrous Metals（Mineral Processing Section）
基金
国家自然科学基金资助项目(U1531119)
德州学院科技计划项目(320057)
关键词
硫化铜
精矿品位
灰色理论
预测模型
copper sulfide
concentrate grade
grey theory
prediction model
