Subspace identification by data orthogonalization and model decoupling

DOI10.1016/j.automatica.2004.05.008zbMath1075.93038OpenAlexW2055132773MaRDI QIDQ705467

Alessandro Chiuso, Giorgio Picci

Publication date: 31 January 2005

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2004.05.008

zbMATH Keywords

Statistical analysisSubspace identificationStochastic realizationExogenous inputsStochastic state-space identification

Mathematics Subject Classification ID

Canonical structure (93B10) Identification in stochastic control theory (93E12) Realizations from input-output data (93B15)

Related Items (7)

Linear approximation and identification of MIMO Wiener-Hammerstein systems ⋮ Combined state and parameter estimation for a bilinear state space system with moving average noise ⋮ A note on mode decoupling of linear time-invariant systems using the generalized sign matrix ⋮ Numerical conditioning and asymptotic variance of subspace estimates ⋮ System identification methods for (operational) modal analysis: review and comparison ⋮ Asymptotic variance of subspace methods by data orthogonalization and model decoupling: a comparative analysis ⋮ Asymptotic properties of subspace estimators

Cites Work

• Unnamed Item
• Unnamed Item
• Stochastic realization with exogenous inputs and `subspace-methods' identification
• Asymptotic variance of subspace methods by data orthogonalization and model decoupling: a comparative analysis
• Unit canonical correlations between future and past
• On consistency of subspace methods for system identification
• The asymptotic variance of subspace estimates.
• On the ill-conditioning of subspace identification with inputs
• Numerical conditioning and asymptotic variance of subspace estimates
• Canonical correlation analysis, approximate covariance extension, and identification of stationary time series
• Subspace model identification Part 3. Analysis of the ordinary output-error state-space model identification algorithm
• A realization approach to stochastic model reduction
• Subspace model identification Part 2. Analysis of the elementary output-error state-space model identification algorithm
• Markovian Representation of Stochastic Processes by Canonical Variables
• Representations of jointly stationary stochastic feedback processes†

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