Kernel-based identification of asymptotically stable continuous-time linear dynamical systems

DOI10.1080/00207179.2020.1868580zbMath1492.93039OpenAlexW3118422275MaRDI QIDQ5863750

Fabio Previdi, Simone Formentin, Mirko Mazzoleni, Matteo Scandella

Publication date: 3 June 2022

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207179.2020.1868580

zbMATH Keywords

kernel-based learninglinear identificationcontinuous-time identification

Mathematics Subject Classification ID

System identification (93B30) Linear systems in control theory (93C05) Asymptotic stability in control theory (93D20)

Related Items (1)

Data‐driven mixed‐sensitivity control with automated weighting functions selection

Uses Software

• DLMF
• CONTSID
• IPsolve

Cites Work

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• Maximum entropy properties of discrete-time first-order stable spline kernel
• Kernel methods in system identification, machine learning and function estimation: a survey
• EM-based identification of continuous-time ARMA models from irregularly sampled data
• Direct continuous-time approaches to system identification. Overview and benefits for practical applications
• Identification of nonlinear systems using polynomial nonlinear state space models
• A new kernel-based approach for linear system identification
• Non-parametric methods of system identification
• Frequency-domain system identification using non-parametric noise models estimated from a small number of data sets
• On kernel design for regularized LTI system identification
• A nonparametric kernel-based approach to Hammerstein system identification
• Prediction-error identification of LPV systems: a nonparametric Gaussian regression approach
• System identification using kernel-based regularization: new insights on stability and consistency issues
• Benchmark problems for continuous-time model identification: design aspects, results and perspectives
• Nonlinear system identification via data augmentation
• Refined instrumental variable estimation: maximum likelihood optimization of a unified Box-Jenkins model
• On the estimation of transfer functions, regularizations and Gaussian processes-revisited
• Recursive Estimation and Time-Series Analysis
• On the estimation of continuous time transfer functions
• Kernels for Linear Time Invariant System Identification
• Parametric identification of transfer functions in the frequency domain-a survey
• Frequency domain system identification with missing data
• Generalized System Identification with Stable Spline Kernels
• Continuous-Time DC Kernel—A Stable Generalized First-Order Spline Kernel
• A New Kernel-Based Approach for NonlinearSystem Identification
• Stable spline identification of linear systems under missing data

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