Pages that link to "Item:Q2071236"

The following pages link to Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition (Q2071236):

Displaying 38 items.

• Using a Volterra system model to analyze nonlinear response in video-packet transmission over IP networks (Q857018) (← links)
• Matrix output extension of the tensor network Kalman filter with an application in MIMO Volterra system identification (Q1626922) (← links)
• A tensor network Kalman filter with an application in recursive MIMO Volterra system identification (Q1680889) (← links)
• Multi-innovation gradient estimation algorithms and convergence analysis for feedback nonlinear equation-error moving average systems (Q2096137) (← links)
• Parameter estimation for a controlled autoregressive autoregressive moving average system based on a recursive framework (Q2110838) (← links)
• Perception-based \(\ell _p\)-norm minimization approach for nonlinear system identification in GGD noise (Q2405551) (← links)
• Local Quadratic Model Tree with Orthogonal Matching Pursuit (LOQUMOTOMP) Method for Nonlinear System Identification (Q5398019) (← links)
• Application of Volterra and Wiener theories for nonlinear parameter estimation in a rotor-bearing system (Q5931188) (← links)
• An efficient numerical algorithm for solving nonlinear Volterra integral equations in the reproducing kernel space (Q6046879) (← links)
• Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion (Q6053746) (← links)
• Instrumental variable‐based multi‐innovation gradient estimation for nonlinear systems with scarce measurements (Q6054508) (← links)
• Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems (Q6063758) (← links)
• Modeling nonlinear systems using the tensor network B‐spline and the multi‐innovation identification theory (Q6069269) (← links)
• Auxiliary model‐based recursive least squares algorithm for two‐input single‐output Hammerstein output‐error moving average systems by using the hierarchical identification principle (Q6069288) (← links)
• Identification of dual‐rate sampled errors‐in‐variables systems with time delays (Q6081006) (← links)
• Generalized continuous mixed <i>p</i>‐norm based sliding window algorithm for a bilinear system with impulsive noise (Q6090194) (← links)
• Least squares parameter estimation and multi-innovation least squares methods for linear fitting problems from noisy data (Q6099491) (← links)
• Separable synthesis gradient estimation methods and convergence analysis for multivariable systems (Q6099494) (← links)
• The data-filtering based bias compensation recursive least squares identification for multi-input single-output systems with colored noises (Q6099840) (← links)
• Iterative parameter identification algorithms for transformed dynamic rational fraction input-output systems (Q6133112) (← links)
• Parameter estimation for a class of time‐varying systems with the invariant matrix (Q6149782) (← links)
• Parameter estimation of multiple‐input single‐output Hammerstein controlled autoregressive system based on improved adaptive moment estimation algorithm (Q6154590) (← links)
• Identification of dual‐rate sampled nonlinear systems based on the cycle reservoir with regular jumps network (Q6190375) (← links)
• Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea (Q6193184) (← links)
• Multi‐innovation gradient‐based iterative identification methods for feedback nonlinear systems by using the decomposition technique (Q6194657) (← links)
• Highly‐computational hierarchical iterative identification methods for multiple‐input multiple‐output systems by using the auxiliary models (Q6194874) (← 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)
• Relaxation of the rank-1 tensor approximation using different norms (Q6611979) (← 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)