摘要
针对传统模糊系统存在的结构难以确定和参数辨识复杂的问题,提出了一种基于混合聚类算法的模糊函数系统辨识算法.与一般的模糊函数系统相比,混合聚类算法结合模糊C均值和模糊C回归模型聚类算法的样本距离.在模型预测部分,采用高斯函数计算每个输入变量的隶属度,利用输入变量隶属度的模糊化算子得到输入向量的隶属度.应用于Box-Jenkins煤气炉数据、一个双入单出的非线性系统和Mackey-Glass混沌时间序列数据的试验结果表明,本文算法具有很好的辨识效果,从而验证了本文算法的有效性与实用性.
A fuzzy function system identification algorithm based on hybrid clustering method is proposed on account of the problems of uncertain structure and parameter identification complexity of conventional fuzzy system.Compared with general fuzzy function systems,the new algorithm combines with the sample distance of fuzzy C-means and fuzzy C-regression model.In model prediction section,the membership degree of each input variable is computed by Gaussian function,and the input vector＇s membership degree is obtained by the fuzziness operator of input variable＇s membership degree.The algorithm is applied separately to Box-Jenkins gas furnace data,a double entry single output nonlinear system and Mackey-Glass chaotic time series.Experimental results show that this algorithm has good identification effect,which validates the effectiveness and practicability of the algorithm.
出处
《信息与控制》
CSCD
北大核心
2011年第3期387-392,400,共7页
Information and Control
基金
华北电力大学留学回国人员科研基金资助项目(200814002)
关键词
模糊函数
模糊C均值
模糊C回归模型
模糊辨识
最小二乘
fuzzy function
fuzzy C-means
fuzzy C-regression model
fuzzy identification
least square
