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202105001.pdf
文章编号： 1672-6987（2021）05-0001-13； DOI： 10.16351/j.1672-6987.2021.05.001
丁锋1,2, 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： 利用迭代搜索和递阶辨识原理,以线性回归系统为例,讨论了递阶梯度迭代辨识方法、递阶多新息梯度迭代辨识方法、递阶最小二乘迭代辨识方法、递阶多新息最小二乘迭代辨识方法,并给出引入加权因子和遗忘因子的递阶迭代辨识方法等。这些迭代辨识方法可以推广到有色噪声的随机系统中。
关键词： 参数估计; 迭代辨识; 多新息辨识; 递阶辨识; 最小二乘; 线性回归模型
中图分类号： TP 273文献标志码： A
引用格式： 丁锋, 刘喜梅. 传递函数辨识(22): 线性回归系统的递阶迭代参数估计［J］. 青岛科技大学学报(自然科学版), 2021, 42(5): 1-13.
DING Feng, LIU Ximei. Transfer function identification. Part V: Hierarchical iterative parameter estimation for linear regressive models ［J］. Journal of Qingdao University of Science and Technology (Natural Science Edition), 2021, 42(5): 1-13.
Transfer Function Identification. Part V: Hierarchical Iterative
Parameter Estimation for Linear Regressive Models
DING Feng1,2, 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: By means of the iterative search and the hierarchical identification principle, this paper investigates the hierarchical gradient-based iterative identification methods, the hierarchical multi-innovation gradient-based iterative identification methods, the hierarchical least squares-based iterative identification methods, and the hierarchical multi-innovation least squares-based iterative identification methods for linear regressive systems. Also, we introduce the weighting factor and the forgetting factor to obtain various weighting and forgetting factor hierarchical iterative identification methods. These iterative methods can be extended to stochastic systems with colored noises.
Key words: parameter estimation; iterative identification; multi-innovation identification; hierarchical identification; least squares; linear regressive model
收稿日期： 2021-08-05
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.
