基于递阶原理的非均匀采样非线性系统的模糊辨识  

Fuzzy Identification for Non-Uniformly Sampled Nonlinear Systems Based on Hierarchical Principle

作　　者：王宏伟[1] 连捷[1] 夏浩[1] WANG Hong-wei;LIAN Jie;XIA Hao(School of Control Science and Engineering,Dalian University of Technology,Dalian,Liaoning 116024,China)

机构地区：[1]大连理工大学控制科学与工程学院,辽宁大连116024

出　　处：《电子学报》2018年第4期1005-1011,共7页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61004040)

摘　　要：针对非均匀多采样率非线性系统的建模问题,提出了基于递阶原理的模糊辨识方法.首先,分析了非线性系统在输入信号非均匀周期刷新,输出信号周期采样的情况下,非线性系统可以通过提升技术,利用多个局部线性模型加权组合的模糊模型来描述.在此基础上,利用GK模糊聚类确定模糊模型前件结构,利用基于递阶原理的递推最小二乘辨识算法辨识模糊模型后件参数.同时,通过鞅定理对辨识算法的收敛性进行了研究.最后,通过仿真实例证明了本文方法的有效性.For the modeling issue of non-uniformly multi-rates sampled nonlinear systems,a fuzzy identification method based on hierarchical principle is proposed in the paper.First of all,a nonlinear system is described as a weighted combination representation of the multiple local linear models by using lift technology when the non-uniformly updating scheme for input signals and uniformly sampling scheme for output signals are taken in the data sampling process.On this basis,we propose a fuzzy identification algorithm,in which the GK fuzzy clustering method and a recursive least squared method based on hierarchical principle are used to confirm the premise structure and consequence parameters of fuzzy model,respectively.Moreover,the convergence of the identification algorithm is studied by using martingale theorem.Finally,the effectiveness of the proposed method is demonstrated by a simulation example.

关 键 词：模糊辨识 非均匀采样 非线性系统 递阶原理 系统辨识 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]