Kernel-based identification using Lebesgue-sampled data

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Kernel methods in system identification, machine learning and function estimation: a survey
Direct continuous-time approaches to system identification. Overview and benefits for practical applications
On identification of FIR systems having quantized output data
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
On the convergence properties of the EM algorithm
Identification of linear dynamic systems using piecewise constant exitations: Use, misuse and alternatives
A new kernel-based approach to system identification with quantized output data
Event-triggered identification of FIR systems with binary-valued output observations
Regularized system identification. Learning dynamic models from data
Iterative learning control for intermittently sampled data: monotonic convergence, design, and applications
Implementation of algorithms for tuning parameters in regularized least squares problems in system identification
On the estimation of transfer functions, regularizations and Gaussian processes-revisited
Kernels for Linear Time Invariant System Identification
Identification Using Binary Measurements for IIR Systems
Identification of Linear Models From Quantized Data: A Midpoint-Projection Approach
The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting
The advantages of directly identifying continuous-time transfer function models in practical applications
A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
Kernel-based identification of asymptotically stable continuous-time linear dynamical systems

This page was built for publication: Kernel-based identification using Lebesgue-sampled data