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文章编号： 1672-6987（2023）02-0001-12； DOI： 10.16351/j.1672-6987.2023.02.001
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： 针对输出误差自回归滑动平均系统, 即Box-Jenkis系统,利用滤波辨识理念,研究和提出了滤波辅助模型广义增广随机梯度辨识方法、滤波辅助模型多新息广义增广随机梯度辨识方法、滤波辅助模型多新息广义增广投影辨识方法、滤波辅助模型递推广义增广梯度辨识方法、滤波辅助模型多新息递推广义增广梯度辨识方法、滤波辅助模型递推广义增广最小二乘辨识方法、滤波辅助模型多新息递推广义增广最小二乘辨识方法。这些滤波辅助模型广义增广辨识方法可以推广到其他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; 递推辨识; 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(5): 输出误差自回归滑动平均系统的滤波辅助模型递推广义增广参数估计［J］. 青岛科技大学学报(自然科学版), 2023, 44(2): 1-12.
DING Feng, LUAN Xiaoli, XU Ling, et al. Filtering identification. Part E: Filtering-based auxiliary model recursive generalized extended parameter estimation for output-error autoregressive moving average systems［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2023， 44（2）： 1-12.
Filtering Identification. Part E: Filtering-Based Auxiliary
Model Recursive Generalized Extended Parameter Estimation for
Output-Error Autoregressive Moving Average Systems
DING Feng1,2, LUAN Xiaoli, 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: For output-error autoregressive moving average systems, i.e., Box-Jenkins systems, this paper presents a filtering-based auxiliary model generalized extended stochastic gradient identification method, a filtering-based auxiliary model multi-innovation generalized extended stochastic gradient identification method, a filtering-based auxiliary model multi-innovation generalized extended projection identification method, a filtering-based auxiliary model recursive generalized extended gradient identification method, a filtering-based auxiliary model multi-innovation recursive generalized extended gradient identification method, a filtering-based auxiliary model recursive generalized extended least squares identification method, and a filtering-based auxiliary model multi-innovation recursive generalized extended least squares identification method by using the filtering identification idea. The proposed filtering-based auxiliary model recursive generalized extended identification methods can be generalized to other linear and nonlinear multivariable stochastic systems with colored noises.
Key words: parameter estimation; recursive identification; auxiliary model identification; multi-innovation identification; hierarchical identification; least squares; stochastic system
收稿日期： 2023-03-08
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.
