Separation identification approach for the <scp>Hammerstein‐Wiener</scp> nonlinear systems with process noise using correlation analysis

DOI10.1002/rnc.6731OpenAlexW4368365677MaRDI QIDQ6154708

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Publication date: 12 March 2024

Published in: International Journal of Robust and Nonlinear Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/rnc.6731

zbMATH Keywords

colored noisecorrelation analysisdata filtering techniqueHammerstein-Wiener nonlinear systemsseparation identification

Mathematics Subject Classification ID

System identification (93B30) Nonlinear systems in control theory (93C10)

Cites Work

• Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models
• Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling
• Data-driven predictive control of Hammerstein-Wiener systems based on subspace identification
• Auxiliary model-based least-squares identification methods for Hammerstein output-error systems
• Estimation of an N-L-N Hammerstein-Wiener model
• Linear approximations of nonlinear FIR systems for separable input processes
• A novel learning algorithm of the neuro-fuzzy based Hammerstein-Wiener model corrupted by process noise
• A recursive parameter estimation algorithm for modeling signals with multi-frequencies
• Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
• Modelling and multi-innovation parameter identification for Hammerstein nonlinear state space systems using the filtering technique
• A robust adaptive control method for Wiener nonlinear systems
• Phase-based order separation for Volterra series identification
• Adaptive Digital Control of Hammerstein Nonlinear Systems with Limited Output Sampling
• Decomposition‐based over‐parameterization forgetting factor stochastic gradient algorithm for Hammerstein‐Wiener nonlinear systems with non‐uniform sampling
• Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems
• Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

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