基于模糊聚类的非线性系统模糊辨识算法  

Fuzzy Identification Algorithm for Nonlinear Systems Based on Fuzzy Clustering

作　　者：刘福才[1] 马丽叶[1] 

机构地区：[1]燕山大学电气工程学院,河北秦皇岛066004

出　　处：《控制工程》2007年第6期625-628,共4页Control Engineering of China

摘　　要：针对模糊聚类算法中数据和运算耗时很长,不适于在线建模与控制的问题,基于模糊聚类型隶属函数和EUM方法,提出了一种新的模糊辨识算法。该方法省去了求解聚类中心的迭代过程,计算时间显著减少。采用该方法对Box-Jenkins煤气炉数据和Mackey-Glass混沌时间序列进行了仿真,结果证明了该方法的有效性。To the problems of time-consuming and unsuitability for on-line modeling and control, based on the fuzzy clustering membership function and equalized universe method, a new fuzzy identification algorithm is proposed. The iteration process for researching cluster centers is not necessary, and computation time required to partition a data set into C classes is quitely reduced. The simulation results on gas stove data and Mackey-Glass time series show the effectiveness of the method.

关 键 词：模糊模型辨识 模糊聚类 等分区间法 CPU时间 

分 类 号：TP15[自动化与计算机技术—控制理论与控制工程]