%0 Journal Article
%T Neuro-f uzzy system modeling based on automatic f uzzy clustering<br>
%A Yuangang TANG
%A Fuchun SUN
%A Zengqi SUN
%A <br>
%J 控制理论与应用
%D 2005
%I 
%X A neuro-fuzzy system model based on automatic fuzzy clustering is proposed. A hybrid model identification algorithm is also developed to decide the model structure and model parameters. The algorithm mainly includes three parts :1) Automatic fuzzy C-means (AFCM) , which is applied to generate fuzzy rules automatically , and then fix on the size of the neuro-fuzzy network , by which the complexity of system design is reducesd greatly at the price of the fitting capability; 2)Recursive least square estimation ( RLSE) . It is used to update the parameters of Takagi-Sugeno model , which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network. Finally ,modeling the dynamical equation of the two- link manipulator with the proposed approach is illustrated to validate the feasibility of the method.
%K Neuro-fuzzy system
%K Automatic fuzzy C-means
%K Gradient descent
%K Back propagation
%K Recursive least square estimation
%K Two- link manipulator<br>
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=15F51BA13E2DE71C8BB3FEBA38F1FC78&yid=2DD7160C83D0ACED&vid=38B194292C032A66&iid=0B39A22176CE99FB&sid=CDEBD1ACE0A4C1C1&eid=B47A0E731AF43EB2&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=0