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文章编号： 1672-6987（2024）05-0001-16； DOI： 10.16351/j.1672-6987.2024.05.001
丁锋1,2, 万立娟2, 栾小丽1, 徐玲3, 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;
3. 常州大学 微电子与控制工程学院, 江苏 常州 213159)
摘要： 针对类多变量输出误差自回归滑动平均(M-OEARMA-like)系统,利用滤波辨识理念和递阶辨识原理,研究和提出了滤波辅助模型递阶广义增广梯度迭代辨识方法、滤波辅助模型递阶多新息广义增广梯度迭代辨识方法、滤波辅助模型递阶广义增广最小二乘迭代辨识方法、滤波辅助模型递阶多新息广义增广最小二乘迭代辨识方法。这些滤波辅助模型递阶广义增广迭代参数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输出误差随机系统中。
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; 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(14): 类多变量输出误差ARMA系统的滤波辅助模型递阶广义增广迭代参数辨识［J］. 青岛科技大学学报(自然科学版), 2024, 45(5): 1-16.
DING Feng, WAN Lijuan, LUAN Xiaoli, et al. Filtering identification. Part N: Filtering-based auxiliary-model hierarchical generalized extended iteraitve parameter identification for multivariable output-error ARMA-like systems［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2024， 45（5）： 1-16.
Filtering Identification. Part N: Filtering-Based Auxiliary-Model
Hierarchical Generalized Extended Iterative Parameter Identification
for Multivariable Output-Error ARMA-Like Systems
DING Feng1,2, WAN Lijuan2, LUAN Xiaoli1, XU Ling3, 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;
3. School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213159, China)
Abstract: For multivariable output-error autoregressive moving average-like (M-OEARMA-like) models, this paper investigates and proposes filtered auxiliary-model hierarchical generalized extended gradient-based iterative identification methods, filtered auxiliary-model hierarchical multi-innovation generalized extended gradient-based iterative identification methods,filtered auxiliary-model hierarchical generalized extended least squares-based iterative identification methods, and filtered auxiliary-model hierarchical multi-innovation generalized extended least squares-based iterative identification methods by means of the filtering identification idea and the hierarchical identification principle from available input-output data. These filtered auxiliary-model hierarchical 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; filtering identification; least squares; multivariable system
收稿日期： 2024-09-26
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.
