%0 Journal Article
%T Combination forecasting of sintered ore alkalinity based on grey neural network<br>基于灰色神经网络的烧结矿碱度组合预测
%A BAO Ya-ping
%A MA Jin-yuan
%A SONG Qiang
%A <br>鲍雅萍
%A 马金元
%A 宋 强
%J 控制理论与应用
%D 2008
%I 
%X To predict the alkalinity of sintered ore accurately in sintered process,a combination grey neural network forecasting model of grey neural network is proposed by combining the grey model GM(1,1)with BP(Back Propagation) neural network.Ten factors relating with the sintered ore alkalinity are selected as the input variables.These variables are estimated on grey model GM(1,1)respectively and the alkalinity of sintered ore is forecasted on BP neural network based on all of these estimated data.The results of simulation show that the relative error is less than 0.005%.
%K grey model
%K neural network
%K combination forecasting model
%K sintered ore
%K alkalinity<br>灰色模型
%K 神经网络
%K 组合预测模型
%K 烧结矿
%K 碱度
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=338FD2001DD206D88AD939AE2D467005&yid=67289AFF6305E306&vid=C5154311167311FE&iid=E158A972A605785F&sid=4198A31627C9B2A6&eid=8225A9F184D4F1CA&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=1&reference_num=4