Order estimation for subspace methods

DOI10.1016/S0005-1098(01)00118-2zbMath0983.93065OpenAlexW2158331151MaRDI QIDQ5947627

Dietmar Bauer

Publication date: 10 April 2002

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0005-1098(01)00118-2

zbMATH Keywords

subspace methodsestimationasymptotic propertiessingular valuesLarimore type algorithmspenalty termsystem order

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10)

Related Items (14)

Subspace-based fault detection robust to changes in the noise covariances ⋮ Unnamed Item ⋮ Prediction error identification of linear systems: a nonparametric Gaussian regression approach ⋮ Model order determination using the Hankel matrix of impulse responses ⋮ ESTIMATING LINEAR DYNAMICAL SYSTEMS USING SUBSPACE METHODS ⋮ Subspace identification for non-linear systems with measured-input non-linearities ⋮ Model Order Estimation of a Multivariable Stochastic Process ⋮ The role of vector autoregressive modeling in predictor-based subspace identification ⋮ Asymptotic properties of subspace estimators ⋮ Estimating the system order by subspace methods ⋮ Estimating ARMAX systems for multivariate time series using the state approach to subspace algorithms ⋮ Closed‐loop identification of the time‐varying dynamics of variable‐speed wind turbines ⋮ Comparing the CCA Subspace Method to Pseudo Maximum Likelihood Methods in the case of No Exogenous Inputs ⋮ USING SUBSPACE METHODS FOR ESTIMATING ARMA MODELS FOR MULTIVARIATE TIME SERIES WITH CONDITIONALLY HETEROSKEDASTIC INNOVATIONS

Uses Software

• System Identification Toolbox

Cites Work

• Unnamed Item
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• Statistical analysis of novel subspace identification methods
• Modeling by shortest data description
• Asymptotically efficient selection of the order of the model for estimating parameters of a linear process
• 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
• Analysis of the asymptotic properties of the MOESP type of subspace algorithms
• Fitting autoregressive models for prediction
• Subspace-based parameter estimation of symmetric noncausal autoregressive signals from noisy measurements

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