Correlation analysis-based error compensation recursive least-square identification method for the Hammerstein model
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Publication:4960526
DOI10.1080/00949655.2017.1378655OpenAlexW2757965434MaRDI QIDQ4960526
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Publication date: 23 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2017.1378655
Inequalities; stochastic orderings (60E15) Applications of stochastic analysis (to PDEs, etc.) (60H30) White noise theory (60H40) Foundations of stochastic processes (60G05)
Related Items (2)
Correlation analysis-based parameter learning of Hammerstein nonlinear systems with output noise ⋮ A novel learning algorithm of the neuro-fuzzy based Hammerstein-Wiener model corrupted by process noise
Cites Work
- Convergence of normalized iterative identification of Hammerstein systems
- Identification of linear systems with hard input nonlinearities of known structure
- Linear approximations of nonlinear FIR systems for separable input processes
- Maximum likelihood least squares identification method for input nonlinear finite impulse response moving average systems
- Gradient-based identification methods for Hammerstein nonlinear ARMAX models
- Dual-mode predictive control algorithm for constrained Hammerstein systems
- Neural network approach for identification of Hammerstein systems
- A bias correction method for identification of linear dynamic errors-in-variables models
- Pathwise Convergence of Recursive Identification Algorithms for Hammerstein Systems
- Convergence of the Iterative Hammerstein System Identification Algorithm
- Hammerstein system identification by non-parametric instrumental variables
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