Gradient estimation algorithms for the parameter identification of bilinear systems using the auxiliary model

被引：136

作者：

Ding, Feng ^{[1
,2
,3
]}

Xu, Ling ^{[3
]}

Meng, Dandan ^{[3
]}

Jin, Xue-Bo ^{[4
]}

Alsaedi, Ahmed ^{[5
]}

Hayat, Tasawar ^{[5
]}

机构：

[1] Hubei Univ Technol, Sch Elect & Elect Engn, Wuhan 430068, Peoples R China

[2] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266061, Peoples R China

[3] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Jiangsu, Peoples R China

[4] Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing 100048, Peoples R China

[5] King Abdulaziz Univ, Dept Math, Jeddah 21589, Saudi Arabia

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2020年 / 369卷

基金：

中国国家自然科学基金;

关键词：

Parameter estimation; Gradient search; Iterative algorithm; Measurement information; Bilinear system; State space system; OPTIMAL DIVIDEND PROBLEM; RECURSIVE LEAST-SQUARES; MULTI-INNOVATION; MONOLAYER; DESIGN;

D O I：

10.1016/j.cam.2019.112575

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

For the bilinear system with white noise, the difficulty of identification is that there exists the product term of the state and input in the system. To overcome this difficulty, we derive the input-output representation of a class of special bilinear systems by using the transformation, and present a stochastic gradient (SG) algorithm and a gradient-based iterative algorithm for estimating the parameters of the systems in the case of the known input-output data by means of the auxiliary model. The proposed gradient-based iterative algorithm can generate more accurate parameter estimates than the auxiliary model based SG algorithm. The performance of the proposed algorithms are tested by two numerical examples. (C) 2019 Elsevier B.V. All rights reserved.

引用

页数：13

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