%0 Journal Article
%T T-S Fuzzy Model Identification with Growing and Pruning Rules for Nonlinear Systems<br>规则可生长与修剪的非线性系统T-S模糊模型辨识
%A LIAO Long-Tao
%A LI Shao-Yuan
%A HUANG Guang-Bin
%A <br>廖龙涛
%A 李少远
%A 黄广斌
%J 自动化学报
%D 2007
%I 
%X Offline rule extraction for the T-S fuzzy systems usually gives a fixed number of fuzzy rules,which make it a bot- tleneck for revealing the complexity of nonlinear systems.Thus, due to a growing and pruning strategy of the neural network, in this paper the fuzzy rules are extracted from real-time data and their number is adjusted online by the impact degree of one local model,such that the rules vary with the system dy- namically and more precisely reflect the character of nonlinear systems.Furthermore,the accuracy of the T-S model is guaran- teed by the parameter learning based on a competitive extended Kalman filter(EKF).The entire algorithm presents a completely online identification of the T-S model and gains a structural and parameter adaptability.An example for CSTR identification il- lustrates its good performance.
%K T-S model
%K fuzzy rule
%K growing and pruning
%K average response
%K online identification<br>T-S模型
%K 模糊规则
%K 生长与修剪
%K 平均响应
%K 在线辨识
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=30D08F9EB175C65C&yid=A732AF04DDA03BB3&vid=27746BCEEE58E9DC&iid=F3090AE9B60B7ED1&sid=FB3C6F66BC48DD45&eid=D7B9DD9E919B6180&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=12