Bayesian semiparametric Wiener system identification

DOI10.1016/j.automatica.2013.03.021zbMath1364.93831OpenAlexW2120645948MaRDI QIDQ2356659

Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön

Publication date: 6 June 2017

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2013.03.021

zbMATH Keywords

Gaussian processsemiparametricparticle filterparticle Markov chain Monte Carlo methodblock-oriented modelsancestor samplingWiener system identification

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) 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

Iterative identification methods for input nonlinear multivariable systems using the key-term separation principle, Variable knot-based spline approximation recursive Bayesian algorithm for the identification of Wiener systems with process noise, A new kernel-based approach to system identification with quantized output data, Identification of block-oriented nonlinear systems starting from linear approximations: a survey, Bayesian nonparametric identification of Wiener systems, Multidimensional Rational Covariance Extension with Approximate Covariance Matching, Kernel methods in system identification, machine learning and function estimation: a survey, Structure and parameter identification for Bayesian Hammerstein system, A two-experiment approach to Wiener system identification, Identification of Wiener model with internal noise using a cubic spline approximation-Bayesian composite quantile regression algorithm, Stochastic gradient with changing forgetting factor-based parameter identification for Wiener systems, Bayesian semiparametric Wiener system identification

Uses Software

• Gibbsit

Cites Work

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