Pages that link to "Item:Q6069269"
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The following pages link to Modeling nonlinear systems using the tensor network B‐spline and the multi‐innovation identification theory (Q6069269):
Displaying 13 items.
- Nonlinear system identification with regularized tensor network B-splines (Q2208614) (← links)
- An efficient conjugate gradient based Cholesky CMA‐ES estimation algorithm for nonlinear systems (Q6117622) (← links)
- Decomposition and composition modeling algorithms for control systems with colored noises (Q6498049) (← links)
- A filtering-based recursive extended least squares algorithm and its convergence for finite impulse response moving average systems (Q6545340) (← links)
- A coupled recursive least squares algorithm for multivariable systems and its computational amount analysis by using the coupling identification concept (Q6558256) (← links)
- Hierarchical estimation methods based on the penalty term for controlled autoregressive systems with colored noises (Q6560443) (← links)
- Cauchy kernel correntropy-based robust multi-innovation identification method for the nonlinear exponential autoregressive model in non-Gaussian environment (Q6577233) (← links)
- Decomposition-based maximum likelihood gradient iterative algorithm for multivariate systems with colored noise (Q6577238) (← links)
- Filtered generalized iterative parameter identification for equation-error autoregressive models based on the filtering identification idea (Q6585579) (← links)
- Parameter estimation methods of linear continuous-time time-delay systems from multi-frequency response data (Q6612083) (← links)
- Auxiliary model maximum likelihood gradient-based iterative identification for feedback nonlinear systems (Q6631782) (← links)
- Hierarchical gradient-based iterative parameter estimation algorithms for a nonlinear feedback system based on the hierarchical identification principle (Q6652739) (← links)
- Sliding window iterative identification for nonlinear closed-loop systems based on the maximum likelihood principle (Q6664769) (← links)
