中南大学学报（自然科学版）2023，Vol.54Issue(11)：4370-4379,10.DOI:10.11817/j.issn.1672-7207.2023.11.015
基于预测补偿网络的锌扫选尾矿品位预测
Grade prediction of zinc scavenging tailings based on prediction-compensation network
摘要
Abstract
Aiming at the problem of low prediction accuracy of key performance indicators of froth flotation,a grade prediction method based on prediction-compensation(PC)network was proposed.The prediction-compensation network was divided into two parts.The first part constructed a GRU-based zinc flotation tailings grade prediction model,which made full use of the time series information of the froth image to obtain the initial grade prediction value.In the second part,in order to solve the problem that the input and output of unseen samples were difficult to map accurately,a dynamic residual compensation(DRC)model composed of the residual trigger derivation module and the improved Choquet fuzzy integration(ICFI)aggregation module was established to compensate for the initial grade prediction value to obtain more accurate results.The results show that compared with the traditional neural network,the proposed prediction-compensation network has better fitting ability and stability,and improves the prediction accuracy and reliability.关键词
泡沫浮选/品位预测/预测补偿网络/Choquet模糊积分Key words
froth flotation/grade prediction/prediction-compensation network/Choquet fuzzy integral分类
信息技术与安全科学引用本文复制引用
刘嘉鹏,唐朝晖,钟宇泽,郑锶,向婉蓉..基于预测补偿网络的锌扫选尾矿品位预测[J].中南大学学报（自然科学版）,2023,54(11):4370-4379,10.基金项目
国家自然科学基金资助项目(62171476)(Project(62171476)supported by the National Natural Science Foundation of China) （62171476）
