%0 Journal Article
%T An intelligent integrated-prediction model for components of Pb-Zn agglomerate based on the process neural network(PNN) and the improved grey system(IGS)<br>基于PNN和IGS的铅锌烧结块成分智能集成预测模型
%A WANG Chun-sheng
%A WU Min
%A SHE Jin-hua
%A <br>王春生
%A 吴 敏
%A 佘锦华
%J 控制理论与应用
%D 2009
%I 
%X To deal with the problem of the component prediction for Pb-Zn agglomerate, an intelligent integratedprediction model based on the process neural network(PNN) and the improved grey system(IGS) is presented. First, the component of agglomerate is predicted by PNN and IGS models, and then, a recursive entropy algorithm for the weighting coefficients is devised from the viewpoint of the information theory. The component of Pb-Zn agglomerate is predicted by integrating the two prediction models. Application results show that the integrated model has high prediction accuracy; it predicts the components of agglomerate efficiently and meets the data-completeness requirements for proportioning computation.
%K lead-zinc sintering process
%K prediction of component
%K process neural network
%K improved grey system
%K information entropy
%K intelligent integrated-prediction model<br>铅锌烧结过程
%K 成分预测
%K 过程神经网络
%K 改进灰色系统
%K 信息熵
%K 智能集成预测模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=8EF5F7899F9D110326B5FE659BF3F88E&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=38B194292C032A66&sid=EC34D52BE81085CE&eid=CA9ED1AB4D9E3E04&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=2&reference_num=11