Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

DOI10.1080/00207721.2015.1014448zbMath1345.93160OpenAlexW1978053056MaRDI QIDQ2822325

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Publication date: 30 September 2016

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: http://eprints.whiterose.ac.uk/74670/1/1019.pdf

zbMATH Keywords

parameter estimationsliding windowEEGCMSS algorithmnonlinear time-varying system identificationSWRLS approachtime-varying common-structured (TVCS) model

Mathematics Subject Classification ID

Related Items (3)

Statistical Learning of Nonlinear Stochastic Differential Equations from Nonstationary Time Series using Variational Clustering ⋮ Neural network-based parametric system identification: a review ⋮ A robust model structure selection method for small sample size and multiple datasets problems

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