Structure-preserving model reduction of second-order systems by Krylov subspace methods

被引:0
|
作者
Kang-Li Xu
Ping Yang
Yao-Lin Jiang
机构
[1] Xinjiang University,College of Mathematics and Systems Science
[2] Xi’an Jiaotong University,School of Mathematics and Statistics
关键词
Model reduction; Second-order systems; Strictly dissipative realization; Krylov subspace; error bounds; 78M34; 37N30; 93A15; 93A30;
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学科分类号
摘要
In this paper, structure-preserving model reduction methods for second-order systems are investigated. By introducing an appropriate parameter, the second-order system is represented by a strictly dissipative realization and the H2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_{2}$$\end{document} norm of the strictly dissipative system is discussed. Then, based on the Krylov subspace techniques, two model reduction methods are proposed to reduce the order of the strictly dissipative system. Further, the reduced second-order systems are obtained. Moreover, according to the factorization of the error system, the H2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_{2}$$\end{document} error bounds are represented by the Kronecker product and the vectorization operator. Finally, two numerical examples illustrate the efficiency of our methods.
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页码:305 / 322
页数:17
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