A unifying theory of least‐squares Padé model reduction methods

I. D. Smith; T. Nigel Lucas

doi:10.1243/PIME_PROC_1996_210_442_02

A unifying theory of least‐squares Padé model reduction methods

I. D. Smith, T. Nigel Lucas

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

The apparently different approaches of least-squares parameter-matching Podé model reduction methods are shown to be related via a unifying theory. From the formulation it is possible to show several interesting features of the least-squares approach which lead to a fuller understanding of exactly how the reduced model approximates the system. An error index is derived for the general case and it is shown that a range of system parameter preservation options are available to the user. A numerical example illustrates the main points of the paper.

Original language	English
Pages (from-to)	95-102
Journal	Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
Volume	210
Issue number	2
DOIs	https://doi.org/10.1243/PIME_PROC_1996_210_442_02
Publication status	Published - 1 May 1996

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10.1243/PIME_PROC_1996_210_442_02

Cite this

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title = "A unifying theory of least‐squares Pad{\'e} model reduction methods",

abstract = "The apparently different approaches of least-squares parameter-matching Pod{\'e} model reduction methods are shown to be related via a unifying theory. From the formulation it is possible to show several interesting features of the least-squares approach which lead to a fuller understanding of exactly how the reduced model approximates the system. An error index is derived for the general case and it is shown that a range of system parameter preservation options are available to the user. A numerical example illustrates the main points of the paper.",

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AB - The apparently different approaches of least-squares parameter-matching Podé model reduction methods are shown to be related via a unifying theory. From the formulation it is possible to show several interesting features of the least-squares approach which lead to a fuller understanding of exactly how the reduced model approximates the system. An error index is derived for the general case and it is shown that a range of system parameter preservation options are available to the user. A numerical example illustrates the main points of the paper.

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JO - Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

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