Fast nonlinear model order reduction via associated transforms of high-order volterra transfer functions

Yang Zhang, Haotian Liu, Qing Wang, Neric Fong, Ngai Wong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 49th Annual Design Automation Conference, DAC '12
Pages289-294
Number of pages6
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event49th Annual Design Automation Conference, DAC '12 - San Francisco, CA, United States
Duration: 3 Jun 20127 Jun 2012

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference49th Annual Design Automation Conference, DAC '12
Country/TerritoryUnited States
CitySan Francisco, CA
Period3/06/127/06/12

Keywords

  • analog/RF circuits
  • association of variables
  • model order reduction (MOR)
  • nonlinear system

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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