A novel two-stage estimation algorithm for nonlinear Hammerstein-Wiener systems from noisy input and output data

DOI10.1016/j.jfranklin.2016.12.024zbMath1378.93131OpenAlexW2563377975MaRDI QIDQ1691191

Yan Wang, Ziyun Wang, Zhicheng Ji

Publication date: 15 January 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2016.12.024

zbMATH Keywords

identificationnonlinear Hammerstein-Wiener systemstwo-stage estimation algorithm

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)

Related Items (4)

Communication-reducing algorithm of distributed least mean square algorithm with neighbor-partial diffusion ⋮ Bias-compensated least squares and fuzzy PSO based hierarchical identification of errors-in-variables Wiener systems ⋮ A novel learning algorithm of the neuro-fuzzy based Hammerstein-Wiener model corrupted by process noise ⋮ Orthotopic-filtering-based fault diagnosis algorithms for nonlinear systems with slowly varying faults

Uses Software

• Wiener-Hammerstein Benchmark

Cites Work

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• Convergence of the auxiliary model-based multi-innovation generalized extended stochastic gradient algorithm for Box-Jenkins systems
• Hierarchical least squares algorithms for nonlinear feedback system modeling
• Comparing some classes of bias-compensating least squares methods
• Recursive identification of errors-in-variables Wiener-Hammerstein systems
• EM algorithm-based identification of a class of nonlinear Wiener systems with missing output data
• A covariance matching approach for identifying errors-in-variables systems
• An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
• Robust fault estimation for singularly perturbed systems with Lipschitz nonlinearity
• Unbiased information filtering for systems with missing measurement based on disturbance estimation
• Multi-innovation stochastic gradient algorithms for dual-rate sampled systems with preload nonlinearity
• Auxiliary model method for transfer function estimation from noisy input and output data
• Parametric identification of parallel Wiener-Hammerstein systems
• On the accuracy of a covariance matching method for continuous-time errors-in-variables identification
• Least squares algorithm for an input nonlinear system with a dynamic subspace state space model
• Identification of errors-in-variables systems with nonlinear output observations
• Recursive identification of errors-in-variables Wiener systems
• DATA FILTERING BASED STOCHASTIC GRADIENT ALGORITHMS FOR MULTIVARIABLE CARAR-LIKE SYSTEMS
• Errors-in-variables methods in system identification

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