A new kernel-based approach to system identification with quantized output data

DOI10.1016/j.automatica.2017.07.053zbMath1375.93132arXiv1610.00470OpenAlexW2529058332MaRDI QIDQ1679846

Gianluigi Pillonetto, Giulio Bottegal, Håkan Hjalmarsson

Publication date: 22 November 2017

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.00470

zbMATH Keywords

system identificationGibbs samplerexpectation-maximizationkernel-based methodsquantized data

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Estimation and detection in stochastic control theory (93E10) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Identification in stochastic control theory (93E12)

Related Items (6)

Identification and adaptation with binary-valued observations under non-persistent excitation condition ⋮ Informative input design for kernel-based system identification ⋮ Variational Bayesian inference for the identification of FIR systems via quantized output data ⋮ Threshold selection and resource allocation for quantized identification ⋮ Estimation of IIR systems with binary-valued observations ⋮ Bayesian positive system identification: truncated Gaussian prior and hyperparameter estimation

Uses Software

• BayesDA

Cites Work

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