Auxiliary model-based hierarchical stochastic gradient methods for multiple-input multiple-output systems

被引:15
作者
Xing, Haoming [1 ]
Ding, Feng [1 ,2 ]
Pan, Feng [1 ]
机构
[1] Jiangnan Univ, Sch Internet Things Engn, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[2] Jiangnan Univ, Sch Internet Things Engn, Wuxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Parameter estimation; Multivariable system; Stochastic gradient; Auxiliary model; Hierarchical identification; Multi-innovation identification; ESTIMATION ALGORITHM; PARAMETER-ESTIMATION; FAULT-DIAGNOSIS; OPTIMIZATION; TRACKING; PACKAGE; DELAY;
D O I
10.1016/j.cam.2023.115687
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
This paper presents a hierarchical identification model for multiple-input multiple-output (MIMO) systems. An auxiliary model-based hierarchical stochastic gradient (AM-HSG) algorithm is derived by means of the auxiliary model identification idea and the hierarchical identification principle. Furthermore, an auxiliary model-based hierarchical multi-innovation stochastic gra-dient (AM-HMISG) algorithm is derived by utilizing the multi-innovation identification theory. In order to compare the computational efficiency of the AM-HSG and AM-HMISG algorithms for identifying MIMO systems, this paper gives the existing traditional gradient algorithms and discusses the complexity of these algorithms in detail. Finally, the simulation example tests the effectiveness of all four algorithms.
引用
收藏
页数:18
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