Pages that link to "Item:Q2389435"

The following pages link to Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems (Q2389435):

Displaying 25 items.

• Global dynamic optimization with Hammerstein-Wiener models embedded (Q2079688) (← links)
• Nonlinear system identification using fractional Hammerstein-Wiener models (Q2296833) (← links)
• Gradient-based iterative identification for MISO Wiener nonlinear systems: application to a glutamate fermentation process (Q2339054) (← links)
• Stochastic gradient with changing forgetting factor-based parameter identification for Wiener systems (Q2349241) (← links)
• Gradient based estimation algorithm for Hammerstein systems with saturation and dead-zone nonlinearities (Q2428877) (← links)
• Maximum likelihood stochastic gradient estimation for Hammerstein systems with colored noise based on the key term separation technique (Q2429064) (← links)
• Gradient-based identification methods for Hammerstein nonlinear ARMAX models (Q2432376) (← links)
• A novel APSO-aided maximum likelihood identification method for Hammerstein systems (Q2435639) (← links)
• Multistage least squares based iterative estimation for feedback nonlinear systems with moving average noises using the hierarchical identification principle (Q2435648) (← links)
• Newton iterative identification for a class of output nonlinear systems with moving average noises (Q2436948) (← links)
• Gradient-based parameter estimation for input nonlinear systems with ARMA noises based on the auxiliary model (Q2441975) (← links)
• New identification method for Hammerstein models based on approximate least absolute deviation (Q2822267) (← links)
• Gradient-based iterative identification for nonuniform sampling output error systems (Q2846259) (← links)
• Modified stochastic gradient identification algorithms with fast convergence rates (Q2846337) (← links)
• Maximum likelihood parameter estimation algorithm for controlled autoregressive autoregressive models (Q2885561) (← links)
• Global convergence conditions in maximum likelihood estimation (Q4905768) (← links)
• Identification of non-uniformly sampled Wiener systems with dead-zone non-linearities (Q5035675) (← links)
• Identification of Hammerstein–Wiener models with hysteresis front nonlinearities (Q5056575) (← links)
• Convergence Analysis of Weighted Stochastic Gradient Identification Algorithms Based on Latest‐Estimation for ARX Models (Q5194867) (← links)
• Two-Stage Recursive Least Squares Parameter Identification for Cascade Systems with Dead Zone (Q5377271) (← links)
• Parametric identification of ARMAX models with unknown forming filters (Q5862739) (← links)
• Fractional-based stochastic gradient algorithms for time-delayed ARX models (Q6046507) (← links)
• Decomposition‐based over‐parameterization forgetting factor stochastic gradient algorithm for Hammerstein‐Wiener nonlinear systems with non‐uniform sampling (Q6060476) (← links)
• Separation identification approach for the <scp>Hammerstein‐Wiener</scp> nonlinear systems with process noise using correlation analysis (Q6154708) (← links)
• Parameters estimation for the Hammerstein-Wiener models with colored noise based on hybrid signals (Q6558276) (← links)