%0 Journal Article
%T Adaptive recursive kernel learning with application to online quality prediction of industrial rubber mixing process<br>自适应递推核学习及在橡胶混炼过程在线质量预报的工业应用
%A LIU Yi
%A ZHANG Xi-cheng
%A ZHU Ke-hui
%A WANG Hai-qing
%A LI Ping
%A <br>刘毅
%A 张锡成
%A 朱可辉
%A 王海清
%A 李平
%J 控制理论与应用
%D 2010
%I 
%X Mooney viscosity having significant impact on the properties of the polymer is very difficult to be measured online. A new modeling method using two-stage recursive kernel-learning is proposed for online modeling and prediction of Mooney viscosity in the rubber mixing processes. The model can be established online for each recipe and recursively updated to adapt fast changes of the process. In the present method, a novel error evaluation index is formulated based on the mixing properties. The model parameters are online selected adaptively, using the fast leave-one-out cross validation criterion, to overcome the embarrassment of parameter selection. An industrial system named as Smart Mixing Information Integrated & Control System has been developed and successfully applied to several large-scale rubber and tire manufacturers in China. The results of Mooney viscosity online prediction show that the developed method is very efficient and thus has real economic importance for rubber mixing processes.
%K rubber mixing process
%K Mooney viscosity
%K kernel-learning
%K recursive estimation
%K parameter selection
%K cross validation<br>橡胶混炼过程
%K 门尼粘度
%K 核学习
%K 递推估计
%K 参数选择
%K 交叉验证
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=B7CE04A5064E7F5D60492963148B4D24&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=94C357A881DFC066&sid=5568599C60D4BE87&eid=2C20277AC27E4821&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=10