复杂系统的递阶模糊辨识

针对Takagi_Sugeno模糊模型 (T_S模型 )严重的维数灾问题, 借鉴GMDH算法, 提出了一种新的复杂系统递阶模糊辨识方法. 本文首先详细描述了由两输入变量的特殊T_S模型所组成的递阶模糊模型 ;然后提出了具体的辨识该递阶模糊模型的方法. 该方法的特点是 :a)在结构辨识阶段, 用FCM模糊聚类方法评价系统中每个输入变量的重要性, 以便构造合理的递阶模糊模型 ;b)预先合理地确定了所要辨识的参数的初始值, 用扩展卡尔曼滤波方法可很快地得到这些参数. 最后, 给出的仿真实例说明了本文辨识方法的有

Hierarchical Fuzzy Identification for Complex Systems

Drawn ideas from GMDH algorithm, a new approach to hierarchical fuzzy identification for complex systems is presented to overcome the serious limitation of Takagi and Sugeno's model (T_S model), that is the problem of "curse of dimensionality". Firstly, a new hierarchical fuzzy model, which consists of a number of hierarchically connected special T_S type models with two input variables, is described in detail. Then, a concrete method for identifying the hierarchical fuzzy model is also proposed. The main features of the presented method are: a) Fuzzy C_means (FCM) is used to evaluate the significance of each input variable for rationally constructing the hierarchical model in the stage of structure identification; b) Since parameters to be identified are properly determined in advance, they can be obtained rapidly by using extended Kalman filter algorithms. Finally, an example is given to demonstrate the validity of the approach.