摘要
介绍一种基于模糊聚类的模糊辨识方法。首先利用含有聚类准则函数的模糊聚类方法来确定模糊规则数和模型前提参数,然后利用最小二乘法来辨识模型的结论参数,最后采用梯度下降法来调整模型的参数。该方法应用于Box-Jenkins数据仿真实例,仿真结果表明该方法简单有效。
This paper introduces a method of fuzzy identification based on fuzzy clustering.In the method,a fuzzy clustering technique associated with clustering criterion function is used to determine the number of fuzzy rules and the parameters in antecedent part of the model first,and then the parameters in consequent part of the model are identified by means of least square algorithm,at last the gradient descent algorithm is used to adjust the parameters of fuzzy model.This method has been successfully applied to identifying the simulation instance of Box-Jenkins data set,and the results demonstrate its simplicity and effectiveness.
出处
《计算机应用与软件》
CSCD
北大核心
2012年第2期216-217,253,共3页
Computer Applications and Software
关键词
模糊模型辨识
模糊聚类
最小二乘估计
Fuzzy model identification Fuzzy clustering Least square estimation
