Pages that link to "Item:Q1693709"

The following pages link to State-space LPV model identification using kernelized machine learning (Q1693709):

Displaying 12 items.

• Continuous-time identification of periodically parameter-varying state space models (Q313229) (← links)
• Prediction-error identification of LPV systems: a nonparametric Gaussian regression approach (Q1716507) (← links)
• Towards efficient maximum likelihood estimation of LPV-SS models (Q1716556) (← links)
• MIMO LPV state-space identification of open-flow irrigation canal systems (Q1955397) (← links)
• Direct identification of continuous-time LPV state-space models via an integral architecture (Q2151932) (← links)
• Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification (Q2307599) (← links)
• Subspace identification of MIMO LPV systems using a periodic scheduling sequence (Q2456509) (← links)
• Identification of state-dependent parameter models with support vector regression (Q3542905) (← links)
• Combined estimation of the parameters and states for a multivariable state‐space system in presence of colored noise (Q5000698) (← links)
• LMI-based design of state-feedback controllers for pole clustering of LPV systems in a union of 𝒟_<i>R</i>-regions (Q5029193) (← links)
• Safe control of nonlinear systems in LPV framework using model-based reinforcement learning (Q6105560) (← links)
• A learning- and scenario-based MPC design for nonlinear systems in LPV framework with safety and stability guarantees (Q6600975) (← links)