Nonlinear system modeling and identification using Volterra‐PARAFAC models

DOI10.1002/acs.1272zbMath1263.93227OpenAlexW1949511783MaRDI QIDQ4908480

Alain Y. Kibangou, Gérard Favier, Thomas Bouilloc

Publication date: 6 March 2013

Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/acs.1272

zbMATH Keywords

Wiener modelsnonlinear system identificationVolterra kernelsPARAFACtensor decompositionsVolterra modelsnonlinear system modelingCLMS algorithmextended complex Kalman filter

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Discrete-time control/observation systems (93C55) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)

Related Items (8)

Alternating Linear Scheme in a Bayesian Framework for Low-Rank Tensor Approximation ⋮ Perception-based \(\ell _p\)-norm minimization approach for nonlinear system identification in GGD noise ⋮ Tensor network alternating linear scheme for MIMO Volterra system identification ⋮ Nonlinear blind identification with three-dimensional tensor analysis ⋮ Nonlinear predictive controller based on S-PARAFAC Volterra models applied to a communicating two-tank system ⋮ Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition ⋮ Phase-based order separation for Volterra series identification ⋮ Volterra series identification and its applications in structural identification of nonlinear block-oriented systems

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