An analysis of the parametrization by data driven local coordinates for multivariable linear systems

DOI10.1016/j.automatica.2004.01.009zbMath1050.93015OpenAlexW1985642899MaRDI QIDQ1879589

Tomas McKelvey, Thomas Ribarits, Manfred Deistler

Publication date: 23 September 2004

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2004.01.009

zbMATH Keywords

system identificationlinear systemsparametrizationstate-space models

Mathematics Subject Classification ID

System identification (93B30) Multivariable systems, multidimensional control systems (93C35)

Related Items

Discussion on: ``Generalized linear dynamic factor models: an approach via singular autoregressions, New method for identifying finite degree Volterra series, 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, Estimating ARMAX systems for multivariate time series using the state approach to subspace algorithms, An LFT approach to parameter estimation

Uses Software

• Matlab

Cites Work

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• An analysis of separable least squares data driven local coordinates for maximum likelihood estimation of linear systems
• Data driven local coordinates for multivariable linear systems and their application to system identification
• An LFT approach to parameter estimation
• Semi-Algebraic Local-Triviality in Semi-Algebraic Mappings
• Parametrizations of linear dynamical systems: Canonical forms and identifiability
• Some geometric questions in the theory of linear systems
• Balanced canonical forms for system identification