%0 Journal Article
%T Drifting Modeling Method Using Weighted Support Vector Machines with Application to Soft Sensor<br>基于加权支持向量机的移动建模方法及其在软测量中的应用
%A FENG Rui
%A ZHANG Yue-Jie
%A ZHANG Yan-Zhu
%A SHAO Hui-He
%A <br>冯瑞
%A 张玥杰
%A 张艳珠
%A 邵惠鹤
%J 自动化学报
%D 2004
%I 
%X The kernel problem in soft sensor of industrial processes is how to build the soft sensor model. However, there exist some questions to some extent in soft sensor model with convention-al modeling methods such as global single model and multiple models. Using the high generaliza-tion ability of support vector machines (SVMs) and the idea of locally weighted learning (LWL) algorithm, this paper proposes a novel learning algorithm named weighted support vector machines (W_ SVMs) which is suitable for local learning. We also present a drifting modeling method based on this algorithm. The proposed modeling method is applied to the estimation of Box-Jenkins gas furnace and FCCU and the simulation results show that the proposed approach is superior to the traditional modeling methods.
%K Support vector machines
%K weighted support vector machines
%K locally weighted learn-ing
%K modeling<br>支持向量机
%K 加权支持向量机
%K 局部加权学习
%K 建模
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=282A980033BE3E02&yid=D0E58B75BFD8E51C&vid=340AC2BF8E7AB4FD&iid=38B194292C032A66&sid=08076B8B3CC96095&eid=5319469C819FCFF1&journal_id=0254-4156&journal_name=自动化学报&referenced_num=6&reference_num=10