Accuracy of linear multiple-input multiple-output (MIMO) models obtained by maximum likelihood estimation
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Publication:417806
DOI10.1016/j.automatica.2012.01.015zbMath1238.93103OpenAlexW1985407349MaRDI QIDQ417806
Graham C. Goodwin, Håkan Hjalmarsson, Juan C. Agüero, Cristian R. Rojas
Publication date: 14 May 2012
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2012.01.015
Multivariable systems, multidimensional control systems (93C35) Estimation and detection in stochastic control theory (93E10) Linear systems in control theory (93C05) Identification in stochastic control theory (93E12)
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Cites Work
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- Asymptotic statistical analysis for model-based control design strategies
- On covariance function tests used in system identification
- Least-squares estimation of a class of frequency functions: a finite sample variance expression
- Matrix differential calculus applied to multiple stationary time series and an extended Whittle formula for information matrices
- Accuracy analysis of time domain maximum likelihood method and sample maximum likelihood method for errors-in-variables and output error identification
- On the equivalence of time and frequency domain maximum likelihood estimation
- Dynamic system identification. Experiment design and data analysis
- Unprejudiced optimal open loop input design for identification of transfer functions
- The information matrix of multiple-input single-output time series models
- The Cramér--Rao lower bound for noisy input--output systems.
- A generalization of Whittle's formula for the information matrix of vector-mixed time series
- Identifiability of errors in variables dynamic systems
- On the frequency domain accuracy of closed-loop estimates
- Guaranteed non-asymptotic confidence regions in system identification
- Black-box identification of multivariable transfer functions—asymptotic properties and optimal input design
- Black-box identification of mimo transfer functions: Asymptotic properties of prediction error models
- Spectral Analysis of Signals: The Missing Data Case
- Asymptotic variance expressions for identified black-box transfer function models
- A general lower bound for parametric spectrum estimation
- Experiment Design for Time Series Analysis: The Multivariate Case
- The fundamental role of general orthonormal bases in system identification
- Fundamental Limitations on the Variance of Estimated Parametric Models
- Finite sample properties of system identification methods
- Variance Error Quantifications That Are Exact for Finite-Model Order
- Input design via LMIs admitting frequency-wise model specifications in confidence regions
- Choosing Between Open- and Closed-Loop Experiments in Linear System Identification
- A Geometric Approach to Variance Analysis in System Identification
- Robustness in Experiment Design
- Errors-in-variables methods in system identification
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