摘要
本文基于RBF神经网络构造了云南某胶磷矿浮选多因素输入和浮选精矿品位、回收率之间的浮选模型,并在Matlab环境下进行了计算机仿真试验,结果表明,模型预测精度较高,验证了非参数建模的合理性,具有一定的实用价值,为浮选过程的控制奠定了基础。
Flotation model between multifactor input of collphanite flotation in Yunnan province,and grade and recovery of flotation concentrate,which was based on RBF neural network,was constructed.Computer simulation tests of the model were carried out in Matlab environment.The results showed that the model had high prediction precision,and the rationality of nonparametric modeling was verified.The model has definite practical value,it settles the foundation for the control of flotation process.
出处
《化工矿物与加工》
CAS
北大核心
2011年第2期1-4,共4页
Industrial Minerals & Processing
关键词
RBF神经网络
胶磷矿浮选模型
仿真
预测
RBF neural network
collphanite flotation model
simulation
prediction
