Pages that link to "Item:Q2349538"

The following pages link to Application of the Newton iteration algorithm to the parameter estimation for dynamical systems (Q2349538):

Displaying 24 items.

• Active fault-tolerant linear parameter varying control for the pitch actuator of wind turbines (Q2407811) (← links)
• The gradient-based iterative estimation algorithms for bilinear systems with autoregressive noise (Q2411712) (← links)
• A recursive least squares parameter estimation algorithm for output nonlinear autoregressive systems using the input-output data filtering (Q2412488) (← links)
• Particle filtering based parameter estimation for systems with output-error type model structures (Q2423919) (← links)
• Fitting the exponential autoregressive model through recursive search (Q2423988) (← links)
• Iterative parameter estimation algorithms for dual-frequency signal models (Q2633182) (← links)
• Weighted parameter estimation for Hammerstein nonlinear ARX systems (Q2697716) (← links)
• Estimation of the Koopman Generator by Newton's Extrapolation (Q4992258) (← links)
• Two-stage multi-innovation stochastic gradient algorithm for multivariate output-error ARMA systems based on the auxiliary model (Q5025871) (← links)
• Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique (Q5025908) (← links)
• Hierarchical parameter and state estimation for bilinear systems (Q5026681) (← links)
• Parameter estimation for a special class of nonlinear systems by using the over-parameterisation method and the linear filter (Q5026890) (← links)
• Generalized Nonlinear Stiffness Identification on Controlled Mechanical Vibrating Systems (Q5215174) (← links)
• Highly computationally efficient state filter based on the delta operator (Q5240975) (← links)
• On some parameter estimation algorithms for the nonlinear exponential autoregressive model (Q5240985) (← links)
• Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory (Q5240987) (← links)
• State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors (Q5241000) (← 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)
• An online identification algorithm of unknown time-varying delay and internal multimodel control for discrete non-linear systems (Q5861102) (← links)
• Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition (Q5965345) (← links)
• Least squares parameter estimation and multi-innovation least squares methods for linear fitting problems from noisy data (Q6099491) (← links)
• Finite‐horizon optimal trajectory control of near space hypersonic vehicle with multi‐constraints (Q6139620) (← links)
• Second-order optimization methods for time-delay autoregressive exogenous models: nature gradient descent method and its two modified methods (Q6496765) (← links)
• Multi-innovation gradient estimation algorithms for multivariate equation-error autoregressive moving average systems based on the filtering technique (Q6598653) (← links)