جهت دسترسی به کاربرگه ی زیر، از این لینک استفاده کنید. http://dl.pgu.ac.ir/handle/Hannan/114378
Title: Data-driven model reduction by moment matching for linear and nonlinear systems
Keywords: Science & Technology;Technology;Automation & Control Systems;Engineering, Electrical & Electronic;Engineering;Model reduction;System identification;Model reduction from data;Moment matching;PROPER ORTHOGONAL DECOMPOSITION;SINGULAR-VALUE ANALYSIS;INTERPOLATION PROBLEM;BALANCED REALIZATION;DEGREE CONSTRAINT;ERROR-BOUNDS;IDENTIFICATION;APPROXIMATIONS;OBSERVABILITY;TRUNCATION;Industrial Engineering & Automation; Mathematical Sciences;Engineering;08 Information And Computing Sciences
Issue Date: 6-Mar-2017
Publisher: Elsevier
place: Engineering & Physical Science Research Council (EPSRC)
Description: Theory and methods to obtain reduced order models by moment matching from input/output data are presented. Algorithms for the estimation of the moments of linear and nonlinear systems are proposed. The estimates are exploited to construct families of reduced order models. These models asymptotically match the moments of the unknown system to be reduced. Conditions to enforce additional properties, e.g. matching with prescribed eigenvalues, upon the reduced order model are provided and discussed. The computational complexity of the algorithms is analyzed and their use is illustrated by two examples: we compute converging reduced order models for a linear system describing the model of a building and we provide, exploiting an approximation of the moment, a nonlinear planar reduced order model for a nonlinear DC-to-DC converter.
URI: http://dl.pgu.ac.ir/handle/Hannan/114378
Other Identifiers: 0005-1098
http://hdl.handle.net/10044/1/43816
https://dx.doi.org/10.1016/j.automatica.2017.01.014
EP/L014343/1
Type Of Material: Other
Appears in Collections:Electrical and Electronic Engineering

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