Maximum likelihood identification of stable linear dynamical systems

DOI10.1016/j.automatica.2018.06.036zbMath1406.93362OpenAlexW2884629900WikidataQ129511612 ScholiaQ129511612MaRDI QIDQ1716471

Jack Umenberger, Thomas B. Schön, Johan Wågberg, Ian R. Manchester

Publication date: 5 February 2019

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2018.06.036

zbMATH Keywords

Lagrangian relaxationconvex optimizationsystem identificationexpectation maximization

Mathematics Subject Classification ID

Semidefinite programming (90C22) Convex programming (90C25) Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05) Linear systems in control theory (93C05) Identification in stochastic control theory (93E12) Software, source code, etc. for problems pertaining to systems and control theory (93-04)

Related Items

Data-driven recursive least squares methods for non-affined nonlinear discrete-time systems, Linear system identification using the sequential stabilizing spline algorithm, Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle, Hierarchical maximum likelihood generalized extended stochastic gradient algorithms for bilinear‐in‐parameter systems, Data filtering based maximum likelihood gradient estimation algorithms for a multivariate equation-error system with ARMA noise, Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory

Uses Software

• System Identification Toolbox
• astsa

Cites Work

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