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202401001.pdf
文章编号： 1672-6987（2024）01-0001-14； DOI： 10.16351/j.1672-6987.2024.01.001
丁锋1,2, 万立娟2, 栾小丽1, 徐玲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： 针对多变量Box-Jenkins模型, 即多变量输出误差自回归滑动平均(M-OEARMA)系统,利用滤波辨识理念和辅助模型辨识思想, 研究和提出了滤波辅助模型递阶广义增广随机梯度辨识方法、滤波辅助模型递阶多新息广义增广随机梯度辨识方法、滤波辅助模型递阶广义增广递推梯度辨识方法、滤波辅助模型递阶多新息广义增广递推梯度辨识方法、滤波辅助模型递阶广义增广最小二乘辨识方法、滤波辅助模型递阶多新息广义增广最小二乘辨识方法。这些滤波辅助模型递阶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; 滤波
辨识; 最小二乘; 多变量系统
中图分类号：TP 273文献标志码： A
引用格式： 丁锋, 万立娟, 栾小丽， 等. 滤波辨识(10): 多变量Box-Jenkins系统的滤波辅助模型递阶广义增广参数辨识［J］. 青岛科技大学学报(自然科学版), 2024, 45(1): 1-14.
DING Feng, WAN Lijuan, LUAN Xiaoli, et al. Filtering identification. Part J: Filtering-based auxiliary model hierarchical generalized extended parameter identification for multivariable Box-Jenkins systems［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2024， 45（1）： 1-14.
Filtering Identification. Part J: Filtering-Based Auxiliary Model
Hierarchical Generalized Extended Parameter Identification for
Multivariable Box-Jenkins Systems
DING Feng1,2, WAN Lijuan2, LUAN Xiaoli1, XU Ling1, 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 multivariable output-error autoregressive moving average (M-OEARMA) models, which are also called multivariable Box-Jenkins models, this paper investigates and proposes filtered auxiliary model hierarchical generalized extended stochastic gradient identification methods, filtered auxiliary model hierarchical multi-innovation generalized extended stochastic gradient identification methods, filtered auxiliary model hierarchical generalized extended recursive gradient identification methods, filtered auxiliary model hierarchical multi-innovation generalized extended recursive gradient identification methods, filtered auxiliary model hierarchical generalized extended least squares identification methods, and filtered auxiliary model hierarchical multi-innovation generalized extended least squares identification methods by using the filtering identification idea and the auxiliary identification idea from available input-output data. These filtered auxiliary model hierarchical generalized extended 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; filtering identification; least squares; multivariable system
收稿日期： 2023-11-26
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.
