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文章编号： 1672-6987（2023）01-0001-11； DOI： 10.16351/j.1672-6987.2023.01.001
丁锋1,2, 栾小丽1， 刘喜梅2(1. 江南大学 物联网工程学院, 江苏 无锡 214122;
2. 青岛科技大学 自动化与电子工程学院, 山东 青岛 266061)
摘要： 受控自回归自回归模型也称为方程误差自回归模型。利用采集的批量数据和迭代搜索, 论文基于滤波辨识理念, 研究和提出了方程误差自回归系统的滤波广义梯度迭代辨识方法、滤波多新息广义梯度迭代辨识方法、滤波广义最小二乘迭代辨识方法、滤波多新息广义最小二乘迭代辨识方法。这些滤波广义迭代辨识方法可以推广到其它有色噪声干扰下的线性和非线性多变量随机系统中。
关键词： 参数估计; 迭代辨识; 多新息辨识; 递阶辨识; 最小二乘; 梯度搜索; 随机系统
中图分类号： TP 273文献标志码： A
引用格式： 丁锋, 栾小丽， 刘喜梅. 滤波辨识(4): 方程误差自回归系统的滤波广义迭代参数估计［J］. 青岛科技大学学报(自然科学版), 2023, 44(1): 1-11.
DING Feng, LUAN Xiaoli, LIU Ximei. Filtering identification. Part D: Filtering-based generalized iterative parameter estimation for equation-error autoregressive systems［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2023， 44（1）： 1-11.
Filtering Identification. Part D: Filtering-Based Generalized Iterative Parameter
Estimation for Equation-Error Autoregressive Systems
DING Feng1,2, LUAN Xiaoli1, LIU Ximei2
(1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)
Abstract: The controlled autoregressive autoregressive model is also called as the equation-error autoregressive model. By using the collected batch data and the iterative search, and based on the filtering identification idea, the paper studies and proposes the filtered generalized gradient-based iterative identification method, the filtered multi-innovation generalized gradient-based iterative identification method, the filtered generalized least squares-based iterative identification method, and filtered multi-innovation generalized least squares-based iterative identification method for equation-error autoregressive systems described by the equation-error autoregressive models. These filtered generalized iterative identification methods can be extended to other linear and nonlinear multivariable stochastic systems with colored noises.
Key words: parameter estimation; iterative identification; multi-innovation identification; hierarchical identification; least squares; gradient search; stochastic system
收稿日期： 2022-12-04
基金项目： 国家自然科学基金项目(61472195).
作者简介： 丁锋(1963—)，男，博士，“泰山学者”特聘教授，博士生导师.
