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**Harvard**

Rahrovani, S., Khorsand Vakilzadeh, M. och Abrahamsson, T. (2012) *On Grammian-based reduction methods for moderate size systems*.

** BibTeX **

@conference{

Rahrovani2012,

author={Rahrovani, Sadegh and Khorsand Vakilzadeh, Majid and Abrahamsson, Thomas},

title={On Grammian-based reduction methods for moderate size systems},

booktitle={19th International Congress on Sound and Vibration 2012, ICSV 2012},

isbn={978-162276465-5},

pages={73-80},

abstract={Over the last decades, there has been a constantly increasing interest in the compact reduced dynamical models. The central idea of model reduction is to systematically capture the main input-output properties by a much simpler model than needed for describing the entire states of the system. Among the most popular model reduction approaches, particularly in systems in the order of a couple of thousands, singular value decomposition based are most common model reduction schemes. In this note, a survey of Grammian-based model reduction techniques for moderate size systems is presented. Comments regarding their properties and discussion about their computational issues are given. Computational efforts needed in reduction methods based on Sylvester and Lyapunov equation are being compared. This investigation is followed by a numerical moderate-size example with dense clusters of close eigenvalues. Finally, results of the competing reduction approaches are compared with respect to computational cost and approximation error for same size approximants.},

year={2012},

keywords={Controllability, Grammian, Observability, Singular Value Decomposition (SVD) },

}

** RefWorks **

RT Conference Proceedings

SR Print

ID 176438

A1 Rahrovani, Sadegh

A1 Khorsand Vakilzadeh, Majid

A1 Abrahamsson, Thomas

T1 On Grammian-based reduction methods for moderate size systems

YR 2012

T2 19th International Congress on Sound and Vibration 2012, ICSV 2012

SN 978-162276465-5

SP 73

OP 80

AB Over the last decades, there has been a constantly increasing interest in the compact reduced dynamical models. The central idea of model reduction is to systematically capture the main input-output properties by a much simpler model than needed for describing the entire states of the system. Among the most popular model reduction approaches, particularly in systems in the order of a couple of thousands, singular value decomposition based are most common model reduction schemes. In this note, a survey of Grammian-based model reduction techniques for moderate size systems is presented. Comments regarding their properties and discussion about their computational issues are given. Computational efforts needed in reduction methods based on Sylvester and Lyapunov equation are being compared. This investigation is followed by a numerical moderate-size example with dense clusters of close eigenvalues. Finally, results of the competing reduction approaches are compared with respect to computational cost and approximation error for same size approximants.

LA eng

OL 30