1 citations · 1 across the 2 of their papers we have counts for
4 papers
Efficient identification of linear, parameter-varying, and nonlinear systems with noise models
Alberto Bemporad, Roland Tóth
We present a general system identification procedure capable of estimating of a broad spectrum of state-space dynamical models, including linear time-invariant (LTI), linear parame…
A Direct State-Space Realization of Discrete-Time Linear Parameter-Varying Input-Output Models
Johan Kon, Roland Tóth, Jeroen van de Wijdeven +2
A minimal state-space (SS) realization of an identified linear parameter-varying (LPV) input-output (IO) model usually introduces dynamic and nonlinear dependency of the state-spac…
Port-Hamiltonian Neural Networks with Output Error Noise Models
Sarvin Moradi, Gerben I. Beintema, Nick Jaensson +2
Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods that utilize Hamiltonian theory as foundational knowledge within neural net…
On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach
Yuhan Liu, Máté Kiss, Roland Tóth +1
While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems where robustness to extrapolation/interpolation errors…