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文章编号： 1672-6987（2022）02-0001-13； DOI： 10.16351/j.1672-6987.2022.02.001
丁锋1,2, 徐玲1， 籍艳2， 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： 利用递阶辨识原理、多新息辨识理论，研究和提出AR-OEARMA系统的辅助模型递阶广义增广随机梯度算法、辅助模型递阶多新息广义增广随机梯度算法、辅助模型递阶广义增广递推梯度算法、辅助模型递阶多新息广义增广递推梯度算法、辅助模型递阶广义增广最小二乘算法、辅助模型递阶多新息广义增广最小二乘算法。这些辅助模型递阶递推辨识方法可以推广到其他有色噪声干扰下的线性多变量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, 徐玲， 籍艳， 等. 传递函数辨识(25):自回归输出误差自回归滑动平均系统的辅助模型递阶广义增广递推参数估计［J］. 青岛科技大学学报(自然科学版), 2022, 43(2): 1-13.
DING Feng, XU Ling, JI Yan, et al. Transfer function identification. Part Y: Auxiliary model hierarchical generalized extended recursive parameter estimation for autoregressive output-error autoregressive moving average systems［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2022， 43（2）： 1-13.
Transfer Function Identification. Part Y: Auxiliary Model Hierarchical
Generalized Extended Recursive Parameter Estimation for Autoregressive
Output-Error Autoregressive Moving Average Systems
DING Feng1,2, XU Ling1, JI Yan2, 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 autoregressive output-error autoregressive moving average systems, this paper presents an auxiliary model hierarchical generalized extended stochastic gradient algorithm, an auxiliary model hierarchical multi-innovation generalized extended stochastic gradient algorithm, an auxiliary model hierarchical generalized extended recursive gradient algorithm, an auxiliary model hierarchical multi-innovation generalized extended recursive gradient algorithm, an auxiliary model hierarchical generalized extended least squares algorithm and an auxiliary model hierarchical multi-innovation generalized extended least squares algorithm by using the hierarchical identification principle and the multi-innovation identification theory. The proposed hierarchical identification methods can be extended 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： 2022-02-24
基金项目： 国家自然科学基金项目(61873111).
作者简介： 丁锋(1963—)，男，博士，“泰山学者”特聘教授，博士生导师.
