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文章编号: 1672-6987(2020)06-0001-12; DOI: 10.16351/j.1672-6987.2020.06.001
丁锋1,2, 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: 针对输出误差系统,利用辅助模型辨识思想和递阶辨识原理,研究了辅助模型递阶随机梯度算法、辅助模型递阶多新息随机梯度算法、辅助模型递阶梯度算法、辅助模型递阶多新息梯度算法、辅助模型递阶最小二乘算法、辅助模型递阶多新息最小二乘算法等。这些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; 递推辨识; 辅助模型辨识; 多新息辨识; 递阶辨识
中图分类号: TP 273文献标志码: A
引用格式: 丁锋, 徐玲, 刘喜梅. 传递函数辨识(17): 输出误差模型的辅助模型递阶递推参数估计[J]. 青岛科技大学学报(自然科学版), 2020, 41(6): 1-12.
DING Feng, XU Ling, LIU Ximei. Transfer function identification. Part Q: Auxiliary model hierarchical parameter estimation for output-error models[J]. Journal of Qingdao University of Science and Technology(Natural Science Edition), 2020, 41(6): 1-12.
Transfer Function Identification. Part Q:
Auxiliary Model Hierarchical Parameter Estimation for Output-Error Models
DING Feng1,2, 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: Based on the auxiliary model identification idea and the hierarchical identification principle, this paper studies the auxiliary model hierarchical stochastic gradient algorithm, the auxiliary model hierarchical multi-innovation stochastic gradient algorithm, the auxiliary model hierarchical gradient algorithm, the auxiliary model hierarchical multi-innovation gradient algorithm, the auxiliary model hierarchical least squares algorithm, and the auxiliary model hierarchical multi-innovation least squares algorithm for output-error systems. These methods can be extended to stochastic output-error systems with colored noises.
Key words: parameter estimation; recursive identification; auxiliary model identification; multi-innovation identification; hierarchical identification
收稿日期: 2020-11-05
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.