Subspace-based prediction of linear time-varying stochastic systems

DOI10.1016/j.automatica.2007.03.029zbMath1138.93060OpenAlexW1997701773MaRDI QIDQ2477697

Kentaro Kameyama, Akira Ohsumi

Publication date: 14 March 2008

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2007.03.029

zbMATH Keywords

subspace identificationsubspace methodstime-varying systemsrecursive algorithmsubspace prediction

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)

Related Items (2)

Nuclear norm-based recursive subspace prediction of time-varying continuous-time stochastic systems via distribution theory ⋮ Subspace identification for a stochastic model of plague

Cites Work

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• Continuous-time frequency domain subspace system identification
• Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
• 4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems
• Subspace algorithms for the stochastic identification problem
• Subspace-based methods for the identification of linear time-invariant systems
• A unifying theorem for three subspace system identification algorithms
• Model predictive control. With a foreword by M. J. Grimble and M. A. Johnson
• Subspace model identification Part 1. The output-error state-space model identification class of algorithms
• Subspace model identification Part 2. Analysis of the elementary output-error state-space model identification algorithm
• Subspace identification for continuous-time stochastic systems via distribution-based approach

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