Estimation of Wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

DOI10.1002/RNC.7224WikidataQ128331679 ScholiaQ128331679MaRDI QIDQ6496261

Shengyi Qian, Bo Li, Feng Li, Naibao He

Publication date: 3 May 2024

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

zbMATH Keywords

parameter estimationadaptive Kalman filterWiener nonlinear systemneural fuzzy modelseparable signals

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10)

Cites Work

• Combined parameter and state estimation algorithms for multivariable nonlinear systems using MIMO Wiener models
• Hierarchical gradient-based identification of multivariable discrete-time systems
• Linear approximations of nonlinear FIR systems for separable input processes
• Adaptive filtering-based recursive identification for time-varying Wiener output-error systems with unknown noise statistics
• Convergence of fixed-point iteration for the identification of Hammerstein and Wiener systems
• A robust adaptive control method for Wiener nonlinear systems
• Parameter estimation for block‐oriented nonlinear systems using the key term separation
• Identification of Wiener models for dynamic and steady‐state performance with application to solid oxide fuel cell
• Subspace based approaches for Wiener system identification
• The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise
• Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

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