A nonparametric kernel-based approach to Hammerstein system identification

DOI10.1016/j.automatica.2017.07.055zbMath1375.93133OpenAlexW2756252287MaRDI QIDQ1679860

Riccardo Sven Risuleo, Giulio Bottegal, Håkan Hjalmarsson

Publication date: 22 November 2017

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218227

zbMATH Keywords

system identificationnonlinear systemsGaussian processesHammerstein systemskernel-based methods

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Identification in stochastic control theory (93E12) Optimal stochastic control (93E20)

Related Items (9)

On semiseparable kernels and efficient implementation for regularized system identification and function estimation ⋮ Deterministic error bounds for kernel-based learning techniques under bounded noise ⋮ A nonparametric kernel-based approach to Hammerstein system identification ⋮ Identification of block-oriented nonlinear systems starting from linear approximations: a survey ⋮ The existence and uniqueness of solutions for kernel-based system identification ⋮ Bayesian nonparametric identification of Wiener systems ⋮ System identification of Hammerstein models by using backward shift algorithm ⋮ Modeling and identification of uncertain-input systems ⋮ Kernel-based identification of asymptotically stable continuous-time linear dynamical systems

Uses Software

• PRMLT

Cites Work

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