Identification of Wiener-Hammerstein systems by \(\ell_1\)-constrained Volterra series

DOI10.1016/J.EJCON.2021.01.002zbMath1458.93053OpenAlexW3125284508MaRDI QIDQ1996668

S. Łagosz, Przemysław Śliwiński, Paweł Wachel

Publication date: 25 February 2021

Published in: European Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejcon.2021.01.002

zbMATH Keywords

convex optimizationsystem identificationVolterra seriesWiener-Hammerstein system

Mathematics Subject Classification ID

System identification (93B30) Nonlinear systems in control theory (93C10) Discrete-time control/observation systems (93C55)

Related Items (2)

Wiener–Hammerstein nonlinear system identification using spectral analysis ⋮ Learning low-dimensional separable decompositions of MIMO non-linear systems

Uses Software

• CVX

Cites Work

• Kernel-based identification of Wiener-Hammerstein system
• Functional aggregation for nonparametric regression.
• Identification of a modified Wiener-Hammerstein system and its application in electrically stimulated paralyzed skeletal muscle modeling
• Regularized nonparametric Volterra kernel estimation
• Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation
• Convex aggregative modelling of infinite memory nonlinear systems
• Nonparametric System Identification
• Fading memory and the problem of approximating nonlinear operators with Volterra series
• The Volterra Representation and the Wiener Expansion: Validity and Pitfalls
• Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation
• Nonlinearity recovery by standard and aggregative orthogonal series algorithms

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