引用本文:张平安 ， 熊学健， 李人厚.基于拟非线性模糊模型的复杂系统模糊辨识[J].控制理论与应用,1998,15(2):286~290.[点击复制]
ZHANG Pingan, XIONG Xuejian and LI Renhou.Quasinonlinear-Fuzzy-Model-Based Fuzzy Identification for Complex Systems[J].Control Theory and Technology,1998,15(2):286~290.[点击复制]
基于拟非线性模糊模型的复杂系统模糊辨识
Quasinonlinear-Fuzzy-Model-Based Fuzzy Identification for Complex Systems
摘要点击 838  全文点击 381  投稿时间：1996-09-16  修订日期：1997-05-08
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DOI编号  
  1998,15(2):286-290
中文关键词  模糊辨识  模糊聚类  卡尔曼滤波
英文关键词  fuzzy identification  fuzzy clustering  kalman filter
基金项目  
作者单位
张平安 ， 熊学健， 李人厚  
中文摘要
      针对一阶Takagi-Sugeno（以下简称T-S）模型辨识复杂系统的困难，本文提出了一种新的拟非线性模糊模型。即在一阶T-S模型的基础上，再进行一次非线性映射。这种模糊模型不仅具有较高的辨识精度，而且具有良好的泛化功能。运用改进的FCM（Fuzzy-C-Means）模糊聚类方法，辨识该模糊模型的结构，与以往的方法比较，极大地简化了结构辨识的复杂性。仿真结果进一步说明了该方法的有效性。
英文摘要
      In this paper, a new Quasinonlinear Fuzzy Model(QNFM) is presented to overcome the fifficulty of the identification of complex systems using the first order Takagi-Sugeno model. Then a nonlinear map is carried out. The presented fuzzy model has the advantages of high identification accuracy and good generalization performance. The structure of the fuzzy model is identified by the modified FCM fuzzy clustering technique, compared with other existing methods, the procedure for finding the optimal structure of the fuzzy model is simplified. The simulation results show that this method is very efficient.