Computing the gradient of the auxiliary quality functional in the parametric identification problem for stochastic systems

DOI10.1134/S0005117911090141zbMath1230.93096OpenAlexW2093514267WikidataQ57604788 ScholiaQ57604788MaRDI QIDQ650040

Yuliya Vladimirovna Tsyganova

Publication date: 25 November 2011

Published in: Automation and Remote Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s0005117911090141

zbMATH Keywords

linear dynamical systemAuxiliary Quality Functional (AQF) methodidentifying of parameterssquare root covariance filter

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Discrete-time control/observation systems (93C55) Linear systems in control theory (93C05) Identification in stochastic control theory (93E12)

Related Items

Differentiating matrix orthogonal transformations, On the computation of derivatives within LD factorization of parametrized matrices, On efficient parametric identification methods for linear discrete stochastic systems, A general approach to constructing parameter identification algorithms in the class of square root filters with orthogonal and \(J\)-orthogonal tranformations

Cites Work

• Unnamed Item
• Unnamed Item
• Maximum likelihood estimation via the extended covariance and combined square-root filters
• Maximum likelihood and prediction error methods
• On unknown state-dependent noise, modeling errors, and adaptive filtering
• Factorization methods for discrete sequential estimation
• Numerical aspects of different Kalman filter implementations
• New square-root algorithms for Kalman filtering
• Likelihood Gradient Evaluation Using Square-Root Covariance Filters
• Maximum likelihood estimation using square root information filters
• A square root formulation of the Kalman covariance equations.