摘要
该文提出一种非线性系统的模型辨识方法 .利用关系聚类法来进行结构辨识 ,从而自动获得模糊规则库 ,并可以得到模糊系统的初始参数 .在聚类的基础上 ,构造一个与之相匹配的模糊神经网络 ,用它的学习算法来训练网络 ,得到一个精确的模糊模型 ,从而实现参数辨识 .通过对两个非线性系统辨识的仿真结果验证了该方法的有效性 .
This paper presents a model identification approach of nonlinear systems. To automatically acquire the fuzzy rule-base and the initial parameters of the fuzzy model, the Relationship Clustering Method is used in structure identification. Based on the cluster result, a fuzzy neural network (FNN) is constructed to match with it. The FNN is trained by its learning algorithm to obtain a precise fuzzy model and realize parameter identification. Finally, the effectiveness of the proposed technique is confirmed by the simulation results of two nonlinear systems.
出处
《计算机学报》
EI
CSCD
北大核心
2004年第4期561-565,共5页
Chinese Journal of Computers
基金
国家中医药管理局基金 ( 2 0 0 0 J P 5 4)资助
