Initializing Wiener-Hammerstein models based on partitioning of the best linear approximation
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Publication:1941257
DOI10.1016/j.automatica.2011.07.007zbMath1260.93167OpenAlexW1971599042MaRDI QIDQ1941257
Publication date: 12 March 2013
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2011.07.007
Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)
Related Items (18)
Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation ⋮ Linear approximation and identification of MIMO Wiener-Hammerstein systems ⋮ A modeling method for complex system using hybrid method ⋮ Kernel-based identification of Wiener-Hammerstein system ⋮ Identification of block-oriented nonlinear systems starting from linear approximations: a survey ⋮ Recursive identification of errors-in-variables Wiener-Hammerstein systems ⋮ Wiener–Hammerstein nonlinear system identification using spectral analysis ⋮ Initial estimates for Wiener-Hammerstein models using phase-coupled multisines ⋮ Identification of stochastic nonlinear models using optimal estimating functions ⋮ Adaptive filtering scheme for parameter identification of nonlinear Wiener–Hammerstein systems and its application ⋮ A Simple Non-linear Transfer Function for a Wiener-Hammerstein Model to Simulate Guitar Distortion and Overdrive Effects ⋮ Initial estimates of the linear subsystems of Wiener-Hammerstein models ⋮ Linear prediction error methods for stochastic nonlinear models ⋮ An improved method for Wiener-Hammerstein system identification based on the fractional approach ⋮ Multi-variable Volterra kernels identification using time-delay neural networks: application to unsteady aerodynamic loading ⋮ Direct identification of the linear block in Wiener system ⋮ Wiener system identification by input injection method ⋮ Parametric identification of parallel Wiener-Hammerstein systems
Uses Software
Cites Work
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- Initial estimates for the dynamics of a Hammerstein system
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- Parametric and nonparametric identification of linear systems in the presence of nonlinear distortions-a frequency domain approach
- Frequency domain system identification with missing data
- Recursive identification method for MISO Wiener-Hammerstein model
- Identifying MIMO Hammerstein systems in the context of subspace model identification methods
- Convergence of the Iterative Hammerstein System Identification Algorithm
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