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Conference Paper: Fast nonlinear model order reduction via associated transforms of high-order volterra transfer functions
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TitleFast nonlinear model order reduction via associated transforms of high-order volterra transfer functions
 
AuthorsZhang, Y1
Liu, H1
Wang, Q1
Fong, N1
Wong, N1
 
KeywordsAssociation of variables
Model order reduction (MOR)
Nonlinear system
Analog/RF circuits
 
Issue Date2012
 
PublisherIEEE Computer Society.
 
CitationThe 49th ACM/EDAC/IEEE Design Automation Conference (DAC 2012), San Francisco, CA., 3-7 June 2012. In ACM/IEEE Design Automation Conference Proceedings, 2012, p. 289-294 [How to Cite?]
 
AbstractWe present a new and fast way of computing the projection matrices serving high-order Volterra transfer functions in the context of (weakly and strongly) nonlinear model order reduction. The novelty is to perform, for the first time, the association of multivariate (Laplace) variables in high-order multiple-input multiple-output (MIMO) transfer functions to generate the standard single-s transfer functions. The consequence is obvious: instead of finding projection subspaces about every s i, only that about a single s is required. This translates into drastic saving in computation and memory, and much more compact reduced-order nonlinear models, without compromising any accuracy. © 2012 ACM.
 
ISBN9781450311991
 
ISSN0738-100X
 
DC FieldValue
dc.contributor.authorZhang, Y
 
dc.contributor.authorLiu, H
 
dc.contributor.authorWang, Q
 
dc.contributor.authorFong, N
 
dc.contributor.authorWong, N
 
dc.date.accessioned2012-09-20T08:16:37Z
 
dc.date.available2012-09-20T08:16:37Z
 
dc.date.issued2012
 
dc.description.abstractWe present a new and fast way of computing the projection matrices serving high-order Volterra transfer functions in the context of (weakly and strongly) nonlinear model order reduction. The novelty is to perform, for the first time, the association of multivariate (Laplace) variables in high-order multiple-input multiple-output (MIMO) transfer functions to generate the standard single-s transfer functions. The consequence is obvious: instead of finding projection subspaces about every s i, only that about a single s is required. This translates into drastic saving in computation and memory, and much more compact reduced-order nonlinear models, without compromising any accuracy. © 2012 ACM.
 
dc.description.naturepublished_or_final_version
 
dc.identifier.citationThe 49th ACM/EDAC/IEEE Design Automation Conference (DAC 2012), San Francisco, CA., 3-7 June 2012. In ACM/IEEE Design Automation Conference Proceedings, 2012, p. 289-294 [How to Cite?]
 
dc.identifier.epage294
 
dc.identifier.hkuros209149
 
dc.identifier.isbn9781450311991
 
dc.identifier.issn0738-100X
 
dc.identifier.scopuseid_2-s2.0-84863551409
 
dc.identifier.spage289
 
dc.identifier.urihttp://hdl.handle.net/10722/165276
 
dc.languageeng
 
dc.publisherIEEE Computer Society.
 
dc.publisher.placeUnited States
 
dc.relation.ispartofACM/IEEE Design Automation Conference Proceedings
 
dc.rightsACM/IEEE Design Automation Conference Proceedings. Copyright © IEEE Computer Society.
 
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.subjectAssociation of variables
 
dc.subjectModel order reduction (MOR)
 
dc.subjectNonlinear system
 
dc.subjectAnalog/RF circuits
 
dc.titleFast nonlinear model order reduction via associated transforms of high-order volterra transfer functions
 
dc.typeConference_Paper
 
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<item><contributor.author>Zhang, Y</contributor.author>
<contributor.author>Liu, H</contributor.author>
<contributor.author>Wang, Q</contributor.author>
<contributor.author>Fong, N</contributor.author>
<contributor.author>Wong, N</contributor.author>
<date.accessioned>2012-09-20T08:16:37Z</date.accessioned>
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<identifier.citation>The 49th ACM/EDAC/IEEE Design Automation Conference (DAC 2012), San Francisco, CA., 3-7 June 2012. In ACM/IEEE Design Automation Conference Proceedings, 2012, p. 289-294</identifier.citation>
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<identifier.issn>0738-100X</identifier.issn>
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<description.abstract>We present a new and fast way of computing the projection matrices serving high-order Volterra transfer functions in the context of (weakly and strongly) nonlinear model order reduction. The novelty is to perform, for the first time, the association of multivariate (Laplace) variables in high-order multiple-input multiple-output (MIMO) transfer functions to generate the standard single-s transfer functions. The consequence is obvious: instead of finding projection subspaces about every s i, only that about a single s is required. This translates into drastic saving in computation and memory, and much more compact reduced-order nonlinear models, without compromising any accuracy. &#169; 2012 ACM.</description.abstract>
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<subject>Association of variables</subject>
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Author Affiliations
  1. The University of Hong Kong