%0 Journal Article
%T Endpoint prediction model of basic oxygen furnace steelmaking based on robust relevance-vector-machines<br>基于鲁棒相关向量机的转炉炼钢终点预报模型
%A HAN Min
%A ZHAO Yao
%A YANG Xi-lin
%A LIN Dong
%A <br>韩敏
%A 赵耀
%A 杨溪林
%A 林东
%J 控制理论与应用
%D 2011
%I 
%X To deal with the problem that the classical relevance vector machine is sensitive to outliers, we present a novel robust relevance vector machine. This machine is applied to predict the endpoint carbon content and temperature of the basic-oxygen-furnace(BOF) steelmaking. Each training sample is assumed to have its individual coefficient of noise variance. With the increase of the prediction error during training procedure, the coefficients of outliers gradually decrease, reducing the impact of outliers. In addition, the iterative formulas for the optimization of hyper-parameters are derived in the Bayesian evidence framework. Simulation results of benchmark test data and the BOF steelmaking data show that the proposed mode achieves high prediction accuracy and good robustness.
%K BOF steelmaking
%K endpoint prediction
%K relevance-vector-machines
%K coefficient of noise variance<br>转炉炼钢
%K 终点预报
%K 相关向量机
%K 噪声方差系数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=ADD173F1A9860DD691972D96EBE1D2A2&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=38B194292C032A66&sid=5957D6E0A50D26B5&eid=406BF8ED3BCE1927&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=17