Pages that link to "Item:Q2312474"

The following pages link to A recursive identification algorithm for Wiener nonlinear systems with linear state-space subsystem (Q2312474):

Displaying 24 items.

• The recursive least squares identification algorithm for a class of Wiener nonlinear systems (Q285760) (← links)
• Combined parameter and state estimation algorithms for multivariable nonlinear systems using MIMO Wiener models (Q328339) (← links)
• Weighted least squares based recursive parametric identification for the submodels of a PWARX system (Q445160) (← links)
• Gaussian approximation in recursive estimation of multiple states of nonlinear Wiener systems (Q1100176) (← links)
• Particle swarm optimization iterative identification algorithm and gradient iterative identification algorithm for Wiener systems with colored noise (Q1654314) (← links)
• Adaptive gradient-based iterative algorithm for multivariable controlled autoregressive moving average systems using the data filtering technique (Q1654319) (← links)
• The bias compensation based parameter and state estimation for observability canonical state-space models with colored noise (Q1712021) (← links)
• Hierarchical Newton iterative parameter estimation of a class of input nonlinear systems based on the key term separation principle (Q1723012) (← links)
• Maximum likelihood recursive least squares estimation for multivariate equation-error ARMA systems (Q1797200) (← links)
• Two-stage gradient-based iterative algorithm for bilinear stochastic systems over the moving data window (Q2205494) (← links)
• Kalman filtering based gradient estimation algorithms for observer canonical state-space systems with moving average noises (Q2423917) (← links)
• Weighted parameter estimation for Hammerstein nonlinear ARX systems (Q2697716) (← links)
• Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique (Q5025908) (← links)
• Recursive identification for multivariate autoregressive equation-error systems with autoregressive noise (Q5027843) (← links)
• The innovation algorithms for multivariable state‐space models (Q5128848) (← links)
• The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle (Q5241002) (← links)
• Decomposition‐based over‐parameterization forgetting factor stochastic gradient algorithm for Hammerstein‐Wiener nonlinear systems with non‐uniform sampling (Q6060476) (← links)
• The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise (Q6073603) (← links)
• Improved gradient descent algorithms for time-delay rational state-space systems: intelligent search method and momentum method (Q6168765) (← links)
• Correlation analysis-based parameter learning of Hammerstein nonlinear systems with output noise (Q6173490) (← links)
• Parameter learning for the nonlinear system described by Hammerstein model with output disturbance (Q6580891) (← links)
• Multi-innovation gradient estimation algorithms for multivariate equation-error autoregressive moving average systems based on the filtering technique (Q6598653) (← links)
• Identification of Hammerstein-Wiener systems with state-space subsystems based on the improved PSO and GSA algorithm (Q6612068) (← links)
• Identification of discrete Wiener systems by using adaptive generalized rational orthogonal basis functions (Q6652025) (← links)