%0 Journal Article
%T Higher-order Takagi-Sugeno fuzzy model based on kernel mapping<br>基于核映射的高阶Takagi-Sugeno模糊模型
%A CAI Qian-feng
%A HAO Zhi-feng
%A YANG Xiao-wei
%A <br>蔡前凤
%A 郝志峰
%A 杨晓伟
%J 控制理论与应用
%D 2011
%I 
%X This paper is concerned with higher-order Takagi-Sugeno(TS) fuzzy systems, where the consequent of a fuzzy rule is a nonlinear combination of input variables. To solve this problem, an implicit nonlinear kernel-mapping is introduced to map the original input space to some higher dimensional feature space, where locally nonlinear submodels of TS fuzzy systems are transformed into locally linear submodels; and then, the expressions of the consequent functions are presented. Furthermore, a novel algorithm of designing higher-order TS fuzzy systems is developed by combining the kernel-based fuzzy clustering with least squares support-vector-machines(LSSVM). Finally, the approximation accuracy, the generalization ability and robustness of the proposed algorithm have been demonstrated by simulation experiments on four well-known data sets.
%K fuzzy systems
%K fuzzy clustering
%K support-vector-machine
%K kernel function<br>模糊系统
%K 模糊聚类
%K 支持向量机
%K 核函数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=C52548B92BC6C5BB1707213281BD4416&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=94C357A881DFC066&sid=8243B77967FFD12E&eid=F4C2D192FB73A21F&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=19