Hammerstein 系统遗忘因子有限窗口分解辨识  

Identification of Hammerstein systems using decomposition based finite-data-window recursive least squares method with a forgetting factor

作　　者：张洋铭 苏豪 刘家尉 ZHANG Yangming;SU Hao;LIU Jawei(General Key Laboratory of Complex System Simulation,Beijing 100000,P.R.China;School of Automation,Chongqing University of Posts and Telecommunications,Chongqing 400065,P.R.China)

机构地区：[1]复杂系统仿真总体重点实验室,北京100000 [2]重庆邮电大学自动化学院,重庆400065

出　　处：《重庆大学学报》2023年第7期36-43,共8页Journal of Chongqing University

基　　金：重庆市自然科学基金资助项目(CSTB2022NSCQ-MSX1225);重庆市教育委员会科学技术研究项目(KJQN202000602);中国博士后科学基金(2022MD713688)。

摘　　要：提出一种带遗忘因子和分解辨识策略的有限数据窗口递归最小二乘Hammerstein系统辨识方法。针对Hammerstein系统具有耦合参数的问题,将Hammerstein系统分解为2个子系统:一个子系统包含线性子系统参数,另一个子系统包含非线性子系统参数;提出一种基于遗忘因子的有限窗口递归最小二乘方法对分解模型进行在线递归估计;仿真示例验证了所提算法能够快速跟踪参数,实现对Hammerstein系统的精确辨识。In this paper,a decomposition based recursive finite-data-window least squares identification method with a forgetting factor is proposed for Hammerstein systems.The proposed method aims to identify the parameters of Hammerstein systems by decomposing them into two subsystems,one involving linear subsystem parameters,and the other containing the nonlinear subsystem parameters.To achieve this,a two-step finite-data-window recursive least squares method with a forgetting factor is developed.To verify the effectiveness and merits of the proposed algorithm,a simulation example is provided,demonstrating that the proposed algorithm can quickly track parameters and accurately and effectively identify Hammerstein systems.

关 键 词：HAMMERSTEIN系统 递归辨识 最小二乘法 遗忘因子 

分 类 号：N945.14[自然科学总论—系统科学]