【作者】 丁园； 高晓智； 黄显林； 尹航；

【Author】 Ding Yuan 1,Gao Xiaozhi1,2,Huang Xianlin1,Yin Hang 1 1.Department of Control Theory and Engineering,Harbin Institute of Technology,Harbin 150001,P.R.China2.Institute of Intelligent Power Electronics,Helsinki University of Technology,Espoo,Finland

【机构】 哈尔滨工业大学控制科学与制导技术研究中心； 芬兰赫尔辛基工业大学智能电力电子研究所；

【摘要】 当采用T-S模糊模型来辨识非线性过程时,通常所采用的T-S模糊模型的规则后件是局部线性或仿射非线性模型。在此基础上辨识得到的T-S模型具有规则数目多的缺点。为了减少模糊规则的数目而同时获得较高的辨识精度,本论文提出了将模糊规则后件中的线性模型用简单多项式模型代替并进一步利用微粒群优化算法辨识规则后件参数的方案。数值仿真表明:同具有线性规则后件的T-S模糊模型相比,应用本文所提出的方案辨识得到的T-S模型具有在相同辨识精度下规则数目显著减少的优点,这一优势随着模型输入变量的增加表现得更为突出。更多还原

【Abstract】 Most of the T-S fuzzy models commonly used in the identification of nonlinear processes have linear or affine consequents.More specifically,the local mathematical models in the consequents of fuzzy rules are taken to be linear or af-fine.However,it can always be observed that the number of fuzzy rules of the resultant T-S fuzzy models is very large.In or-der to reduce the number of fuzzy rules and keep the model accuracy unchanged,a special class of T-S fuzzy models is taken to be the candidate models in this study.In more detail,the consequent of the fuzzy rule in this research is polynomial models instead of linear or affine ones.Based on this candidate T-S fuzzy model,the particle swarm optimization algorithms are em-ployed to estimate the parameters in this model.Numerical simulations demonstrate that the number of fuzzy rules is signifi-cantly reduced while the model accuracy is still unchanged.This advantage comes to be more prominent with the increase of input variables.更多还原