Kernel‐based regularization least squares algorithm for nonlinear time‐delayed systems using self‐organizing maps

DOI10.1002/rnc.6632MaRDI QIDQ6190424

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Publication date: 6 February 2024

Published in: International Journal of Robust and Nonlinear Control (Search for Journal in Brave)

zbMATH Keywords

Volterra seriesself-organizing mapsleast squares algorithmnonlinear time-delayed systemskernel-based regularization method

Mathematics Subject Classification ID

System identification (93B30) Nonlinear systems in control theory (93C10) Delay control/observation systems (93C43)

Cites Work

• Parameter identification for a nonlinear enzyme-catalytic dynamic system with time-delays
• Adaptive sparse Volterra system identification with \(\ell _{0}\)-norm penalty
• Tuning complexity in regularized kernel-based regression and linear system identification: the robustness of the marginal likelihood estimator
• A new kernel-based approach for linear system identification
• Multiparameter regularization for Volterra kernel identification via multiscale collocation methods
• Matrix output extension of the tensor network Kalman filter with an application in MIMO Volterra system identification
• On kernel design for regularized LTI system identification
• An auxiliary model based least squares algorithm for a dual-rate state space system with time-delay using the data filtering
• Model recovery for Hammerstein systems using the hierarchical orthogonal matching pursuit method
• An interactive maximum likelihood estimation method for multivariable Hammerstein systems
• Model recovery for Hammerstein systems using the auxiliary model based orthogonal matching pursuit method
• Computational complexity analysis of set membership identification of Hammerstein and Wiener systems
• On the estimation of transfer functions, regularizations and Gaussian processes-revisited
• Kernel-based methods for Volterra series identification
• Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series
• System Identification Via Sparse Multiple Kernel-Based Regularization Using Sequential Convex Optimization Techniques
• Applied Multivariate Statistical Analysis
• Fading memory and the problem of approximating nonlinear operators with Volterra series
• Analytical Foundations of Volterra Series
• Uniform Approximation of Periodically-Varying Systems
• Recursive methods for estimating the radial basis function‐based state‐dependent autoregressive model
• Stochastic average gradient algorithm for multirate FIR models with varying time delays using self‐organizing maps
• Phase-based order separation for Volterra series identification
• The innovation algorithms for multivariable state‐space models
• Decomposition‐based over‐parameterization forgetting factor stochastic gradient algorithm for Hammerstein‐Wiener nonlinear systems with non‐uniform sampling
• Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems
• Three‐stage multi‐innovation parameter estimation for an exponential autoregressive time‐series model with moving average noise by using the data filtering technique
• Auxiliary model‐based iterative parameter estimation for a nonlinear output‐error system with saturation and dead‐zone nonlinearity

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