Data driven local coordinates for multivariable linear systems and their application to system identification

DOI10.1016/j.automatica.2004.04.015zbMath1055.93019OpenAlexW2083706900MaRDI QIDQ1881214

Thomas Ribarits, Anders Helmersson, Tomas McKelvey

Publication date: 4 October 2004

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2004.04.015

Mathematics Subject Classification ID

System identification (93B30)

Related Items

An analysis of the parametrization by data driven local coordinates for multivariable linear systems, Data-dependent analysis of model validation errors for linear system identification, Identifiability of affine linear parameter-varying models, Two nonlinear optimization methods for black box identification compared, Optimization on a Grassmann manifold with application to system identification, Variance analysis of identified linear MISO models having spatially correlated inputs, with application to parallel Hammerstein models, Constrained state-space system identification with application to structural dynamics, An analysis of separable least squares data driven local coordinates for maximum likelihood estimation of linear systems, Robust maximum-likelihood estimation of multivariable dynamic systems, New developments for matrix fraction descriptions: a fully-parametrised approach

Cites Work

• Inertia characteristics of self-adjoint matrix polynomials
• Fast projection methods for minimal design problems in linear system theory
• On line structure selection for multivariable state-space models
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• Balanced Parametrization of Classes of Linear Systems
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