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文章编号： 1672-6987（2023）03-0001-13； DOI： 10.16351/j.1672-6987.2023.03.001
丁锋1,2, 栾小丽1, 徐玲1, 张霄1, 周怡红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： 输出误差自回归滑动平均模型也称为Box-Jenkins模型。针对Box-Jenkins系统, 基于滤波辨识理念, 研究和提出了滤波辅助模型广义增广梯度迭代辨识方法、滤波辅助模型多新息广义增广梯度迭代辨识方法、 滤波辅助模型多新息广义增广投影迭代辨识方法、 滤波辅助模型广义增广最小二乘迭代辨识方法、滤波辅助模型多新息广义增广最小二乘迭代辨识方法。这些滤波辅助模型广义增广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; 辅助模型辨识; 多新息辨识; 递阶辨识; 最小二乘; 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, 徐玲, 等. 滤波辨识(6): 输出误差自回归滑动平均系统的滤波辅助模型广义增广迭代参数估计［J］. 青岛科技大学学报(自然科学版), 2023, 44(3): 1-13.
DING Feng, LUAN Xiaoli, XU Ling, et al. Filtering identification. Part F: Filtering-based auxiliary model generalized extended iterative parameter estimation for output-error autoregressive moving average systems［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2023， 44（3）： 1-13.
Filtering Identification. Part F: Filtering-Based Auxiliary
Model Generalized Extended Iterative Parameter Estimation for
Output-Error Autoregressive Moving Average Systems
DING Feng1,2, LUAN Xiaoli1, XU Ling1, ZHANG Xiao1, ZHOU Yihong1, 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 output-error autoregressive moving average model is also called the Box-Jenkins model. Based on the filtering identification idea, the paper studies and proposes the filtered auxiliary model generalized extended gradient-based iterative identification method, the filtered auxiliary model multi-innovation generalized extended gradient-based iterative identification method, the filtered auxiliary model multi-innovation generalized extended projection-based iterative identification method, the filtered auxiliary model generalized extended least squares-based iterative identification method, and the filtered auxiliary model multi-innovation generalized extended least squares-based iterative identification method for Box-Jenkins systems from available input-output data. These filtered auxiliary model generalized extended iterative identification methods can be extended to other linear and nonlinear multivariable stochastic systems with colored noises.
Key words: parameter estimation; iterative identification; auxiliary model identification; multi-innovation identification; hierarchical identification; least squares; stochastic system
收稿日期： 2023-05-10
基金项目： 国家自然科学基金项目(62273167).
作者简介： 丁锋(1963—)，男，博士，“泰山学者”特聘教授，博士生导师.
