A novel learning algorithm of the neuro-fuzzy based Hammerstein-Wiener model corrupted by process noise
From MaRDI portal
Publication:1996646
DOI10.1016/j.jfranklin.2020.12.034zbMath1458.93155OpenAlexW3118677888MaRDI QIDQ1996646
Publication date: 25 February 2021
Published in: Journal of the Franklin Institute (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jfranklin.2020.12.034
Fuzzy control/observation systems (93C42) Nonlinear systems in control theory (93C10) Stochastic systems in control theory (general) (93E03)
Related Items
Decentralized adaptive neuro-fuzzy dynamic surface control for maximum power point tracking of a photovoltaic system ⋮ Auxiliary model‐based recursive least squares algorithm for two‐input single‐output Hammerstein output‐error moving average systems by using the hierarchical identification principle ⋮ Separation identification approach for the <scp>Hammerstein‐Wiener</scp> nonlinear systems with process noise using correlation analysis ⋮ Correlation analysis-based parameter learning of Hammerstein nonlinear systems with output noise
Cites Work
- Recursive least squares algorithm and gradient algorithm for Hammerstein-Wiener systems using the data filtering
- Nonlinear predictive control of dynamic systems represented by Wiener-Hammerstein models
- Bias compensation principle based recursive least squares identification method for Hammerstein nonlinear systems
- Data-driven predictive control of Hammerstein-Wiener systems based on subspace identification
- An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
- A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation
- Robust identification of Wiener time-delay system with expectation-maximization algorithm
- A novel two-stage estimation algorithm for nonlinear Hammerstein-Wiener systems from noisy input and output data
- Linear approximations of nonlinear FIR systems for separable input processes
- Adaptive control of stochastic Hammerstein–Wiener nonlinear systems with measurement noise
- Identification Scheme for Hammerstein Output Error models with Bounded Noise
- Dual-mode predictive control algorithm for constrained Hammerstein systems
- On identification of the controlled plants described by the Hammerstein system
- Correlation analysis-based error compensation recursive least-square identification method for the Hammerstein model
- Recursive Identification of Hammerstein Systems: Convergence Rate and Asymptotic Normality
- Recursive identification for multi-input–multi-output Hammerstein–Wiener system
