Linear approximation and identification of MIMO Wiener-Hammerstein systems

DOI10.1016/j.automatica.2016.04.040zbMath1343.93090OpenAlexW2412612281MaRDI QIDQ313184

Tohru Katayama, Hajime Ase

Publication date: 9 September 2016

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2016.04.040

zbMATH Keywords

subspace identificationlinear approximationorthogonal projectionbasis function expansionseparable least-squaresWiener-Hammerstein system

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Multivariable systems, multidimensional control systems (93C35) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)

Related Items (10)

The gradient-based iterative estimation algorithms for bilinear systems with autoregressive noise ⋮ Identification of block-oriented nonlinear systems starting from linear approximations: a survey ⋮ Recursive subspace identification subject to relatively slow time-varying load disturbance ⋮ Subspace-based deterministic identification of MIMO linear state-delayed systems ⋮ The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving average noise ⋮ Auxiliary model based recursive generalized least squares identification algorithm for multivariate output-error autoregressive systems using the decomposition technique ⋮ Adaptive filtering-based recursive identification for time-varying Wiener output-error systems with unknown noise statistics ⋮ The data filtering based generalized stochastic gradient parameter estimation algorithms for multivariate output-error autoregressive systems using the auxiliary model ⋮ Subspace identification of Hammerstein-type nonlinear systems subject to unknown periodic disturbance ⋮ Decomposition-based recursive least squares identification methods for multivariate pseudo-linear systems using the multi-innovation

Uses Software

• Wiener-Hammerstein Benchmark

Cites Work

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