摘要
将改进云模型和改进RBF神经网络相结合,提出了一种预测矿石中金品位的模型。先利用DS证据理论和云模型将定性信息定量化,再采用量子粒子群算法和RBF神经网络完成矿石中金品位预测。结果表明:该模型的均方根误差为0.0092,最大误差为0.0161,相关系数为0.9402,可较好保留定性信息特性,金品位预测效果较好。
A gold grade prediction model was proposed by combining improved cloud models with improved RBF neural networks.Qualitative information was quantified using DS evidence theory and cloud models,and then quantum particle swarm optimization algorithm and RBF neural network were used to predict the gold grade in ores.The results indicate that the mean square error of this model is 0.0092,the maximum error is 0.0161,and the correlation coefficient is 0.9402,the model can better preserve the qualitative information characteristics,the prediction effect of gold grade is good.
作者
梁智霖
郭攀
LIANG Zhilin;GUO Pan(Basic Department of Henan Health Executive College,Zhengzhou 450000,China;School of Water Resources and Civil Engineering,Zhengzhou University,Zhengzhou 450000,China)
出处
《湿法冶金》
CAS
北大核心
2024年第2期195-200,共6页
Hydrometallurgy of China
关键词
金
品位
预测
模型
定性信息
定量信息
云模型
RBF神经网络
gold
grade
prediction
model
qualitative information
quantitative information
cloud model
RBF neural network
作者简介
第一作者:梁智霖(1987-),男,硕士,讲师,主要研究方向为数学建模;通信作者:郭攀(1985-),男,博士,高级工程师,主要研究方向为结构无损检测及数学建模。E-mail:guopan@zzu.edu.cn。
