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标题：A scalable moment matching-based model reduction technique of linear networks 1 1 The research leading to these results has received funding from UEFISCDI Romania, project TE - MoCOBiDS, no. 176/01.10.2015.
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作者：Tudor C. Ionescu ; Ion Necoara
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：8232-8237
DOI：10.1016/j.ifacol.2017.08.1390
语种：English
出版社：Elsevier
摘要：AbstractIn this paper we address the problem of model order reduction of linear network systems. Using Sylvester equation-based moment matching techniques, we propose a framework to compute families of parametrized reduced order models that achieve moment matching and preserve the structure of the to-be-reduced model of the network. Further, using balanced truncation techniques we also reduce the number of subsystems in the network. The result is a low-order approximation of the linear network system with a reduced number of subsystems that exhibit properties similar to the given network. This approach leads to a scalable modeling algorithm for large-scale networks, using specific features of the system, such as the dynamical interactions between subsystems and the concepts from the model order reduction field.
关键词：KeywordsLinear network systemsmodel reductionmoment matchingSylvester equation