Pages that link to "Item:Q2132160"

The following pages link to Piecewise reproducing kernel-based symmetric collocation approach for linear stationary singularly perturbed problems (Q2132160):

Displaying 17 items.

• Hierarchical gradient- and least squares-based iterative algorithms for input nonlinear output-error systems using the key term separation (Q2030993) (← links)
• A continuous kernel functions method for mixed-type functional differential equations (Q2161570) (← links)
• Perturbation and stability analysis of strong form collocation with reproducing kernel approximation (Q2894809) (← links)
• Decomposition strategy-based hierarchical least mean square algorithm for control systems from the impulse responses (Q5028635) (← links)
• Parameter estimation for an exponential autoregressive time series model by the Newton search and multi-innovation theory (Q5028671) (← links)
• Recursive least-squares algorithm for a characteristic model with coloured noise by means of the data filtering technique (Q5028709) (← links)
• Separable multi-innovation Newton iterative modeling algorithm for multi-frequency signals based on the sliding measurement window (Q6042570) (← links)
• Decomposition‐based over‐parameterization forgetting factor stochastic gradient algorithm for Hammerstein‐Wiener nonlinear systems with non‐uniform sampling (Q6060476) (← links)
• Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems (Q6061864) (← links)
• Multi‐innovation Newton recursive methods for solving the support vector machine regression problems (Q6071503) (← links)
• Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems (Q6083767) (← links)
• An accurate numerical technique for fractional oscillation equations with oscillatory solutions (Q6188918) (← links)
• Recursive least squares estimation methods for a class of nonlinear systems based on non-uniform sampling (Q6494672) (← links)
• Filtering-based recursive least squares estimation approaches for multivariate equation-error systems by using the multiinnovation theory (Q6494697) (← links)
• Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output-error systems (Q6495162) (← links)
• Partially-coupled gradient-based iterative algorithms for multivariable output-error-like systems with autoregressive moving average noises (Q6609022) (← links)
• Parameter estimation for a multi-input multi-output state-space system with unmeasurable states through the data filtering technique (Q6611544) (← links)