%0 Journal Article
%T Equivalence between type-2 TSK fuzzy model and uncertain Gaussian mixture model<br>二型Takagi-Sugeno-Kang模糊模型和不确定高斯混合模型的等价性
%A ZHANG Qin-li
%A WANG Shi-tong
%A TAN Zuo-ping
%A <br>张钦礼
%A 王士同
%A 谭左平
%J 控制理论与应用
%D 2009
%I 
%X This work explores how the uncertain Gaussian mixture model(UGMM) can be translated to an additive type-2 TSK(Takagi-Sugeno-Kang) fuzzy logic system. The mathematical equivalence between the conditional mean of a UGMM and the defuzzified output of a type-2 TSK fuzzy model(T2-TSK-FM) is proved. The relationship between a UGMM and a T2-TSK-FM, and the conditions for UGMM to T2-TSK- FM translation is made explicit in the form of a theorem. The proposed results provide a new method for constructing a T2-TSK-FM by interpreting a fuzzy system from a probabilistic viewpoint. Instead of estimating the parameters of the fuzzy rules directly, the parameters of a UGMM are estimated using any popular density estimation algorithm, such as expectation maximization. The proposed approach is also applied to Mackey-Glass chaotic time series. After comparing the simulation results with those obtained with other system modeling tools, it can be claimed that successful results are achieved.
%K type-2 TSK fuzzy model
%K Gaussian mixture model
%K fuzzy system
%K EM(expectation maximization) algorithm<br>二型TSK模糊模型
%K 高斯混合模型
%K 模糊系统
%K 期望值的最大化算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=5E213AF9F3E020DF8AA8C741CEE44BC9&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=0B39A22176CE99FB&sid=50BBDFAC8381694B&eid=847B14427F4BF76A&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=7