Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique

DOI10.1007/s11071-014-1771-9zbMath1331.93211OpenAlexW2081232207MaRDI QIDQ494751

Feng Ding, Yawen Mao

Publication date: 2 September 2015

Published in: Nonlinear Dynamics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11071-014-1771-9

zbMATH Keywords

Hammerstein systemsfiltering techniquestochastic gradientmulti-innovation identificationkey-term separation principle

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) System identification (93B30) Nonlinear systems in control theory (93C10) Identification in stochastic control theory (93E12)

Related Items

System identification application using Hammerstein model ⋮ A novel parameter separation based identification algorithm for Hammerstein systems ⋮ Data filtering-based multi-innovation stochastic gradient algorithm for nonlinear output error autoregressive systems ⋮ A recursive least squares algorithm for pseudo-linear ARMA systems using the auxiliary model and the filtering technique ⋮ Data filtering based forgetting factor stochastic gradient algorithm for Hammerstein systems with saturation and preload nonlinearities ⋮ Combined parameter and state estimation algorithms for multivariable nonlinear systems using MIMO Wiener models ⋮ Biased compensation recursive least squares-based threshold algorithm for time-delay rational models via redundant rule ⋮ Robust identification of Wiener time-delay system with expectation-maximization algorithm ⋮ Recursive least squares algorithm for nonlinear dual-rate systems using missing-output estimation model ⋮ Gradient-based recursive identification methods for input nonlinear equation error closed-loop systems ⋮ Least-squares-based iterative and gradient-based iterative estimation algorithms for bilinear systems ⋮ The gradient-based iterative estimation algorithms for bilinear systems with autoregressive noise ⋮ The Boubaker polynomials and their application to solve fractional optimal control problems ⋮ Recursive parameter identification of the dynamical models for bilinear state space systems ⋮ An efficient conjugate gradient based Cholesky CMA‐ES estimation algorithm for nonlinear systems ⋮ Auxiliary variable-based identification algorithms for uncertain-input models ⋮ Parameter estimation for nonlinear systems by using the data filtering and the multi-innovation identification theory ⋮ High-dimensional time series prediction using kernel-based koopman mode regression ⋮ New gradient based identification methods for multivariate pseudo-linear systems using the multi-innovation and the data filtering ⋮ Decomposition-based gradient estimation algorithms for multivariate equation-error autoregressive systems using the multi-innovation theory ⋮ Recursive and iterative least squares parameter estimation algorithms for multiple-input-output-error systems with autoregressive noise ⋮ The filtering based maximum likelihood recursive least squares estimation for multiple-input single-output systems ⋮ Iterative parameter estimation for signal models based on measured data ⋮ Identification of Hammerstein systems with continuous nonlinearity

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