The quest for the right kernel in Bayesian impulse response identification: the use of OBFs

DOI10.1016/j.automatica.2017.10.007zbMath1378.93130OpenAlexW2767775086MaRDI QIDQ680545

Mohamed Abdelmonim Hassan Darwish, Gianluigi Pillonetto, Roland Tóth

Publication date: 23 January 2018

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2017.10.007

zbMATH Keywords

system identificationregularizationmachine learningreproducing kernel Hilbert spaceorthonormal basis functionsBayesian identification

Mathematics Subject Classification ID

Linear systems in control theory (93C05) Least squares and related methods for stochastic control systems (93E24) Pole and zero placement problems (93B55) Identification in stochastic control theory (93E12) Stochastic stability in control theory (93E15)

Related Items (11)

Kernel-based identification of non-causal systems with application to inverse model control ⋮ On the mathematical foundations of stable RKHSs ⋮ The existence and uniqueness of solutions for kernel-based system identification ⋮ When cannot regularization improve the least squares estimate in the kernel-based regularized system identification ⋮ Prediction-error identification of LPV systems: a nonparametric Gaussian regression approach ⋮ Introducing system identification strategy into model predictive control ⋮ Bayesian positive system identification: truncated Gaussian prior and hyperparameter estimation ⋮ Sparse System Identification in Pairs of Pulse and Takenaka--Malmquist Bases ⋮ Separable multi‐innovation stochastic gradient estimation algorithm for the nonlinear dynamic responses of systems ⋮ Sparse estimation in linear dynamic networks using the stable spline horseshoe prior ⋮ Decomposition strategy-based hierarchical least mean square algorithm for control systems from the impulse responses

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