38 citations · 239 across the 28 of their papers we have counts for
11 papers · 1 filter
Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming
Stefan-Cristian Nechita, Roland Toth, Dhruv Khandelwal +1
Data-driven modeling of nonlinear dynamical systems often require an expert user to take critical decisions a priori to the identification procedure. Recently an automated strategy…
Non-linear State-space Model Identification from Video Data using Deep Encoders
Gerben Izaak Beintema, Roland Toth, Maarten Schoukens
Identifying systems with high-dimensional inputs and outputs, such as systems measured by video streams, is a challenging problem with numerous applications in robotics, autonomous…
Nonlinear state-space identification using deep encoder networks
Gerben Beintema, Roland Toth, Maarten Schoukens
Nonlinear state-space identification for dynamical systems is most often performed by minimizing the simulation error to reduce the effect of model errors. This optimization proble…
Linear Parameter-Varying Embedding of Nonlinear Models with Reduced Conservativeness
Arash Sadeghzadeh, Roland Toth
In this paper, a systematic approach is developed to embed the dynamical description of a nonlinear system into a linear parameter-varying (LPV) system representation. Initially, t…
Affine Linear Parameter-Varying Embedding of Nonlinear Models with Improved Accuracy and Minimal Overbounding
Arash Sadeghzadeh, Bardia Sharif, Roland Toth
In this paper, automated generation of linear parameter-varying (LPV) state-space models to embed the dynamical behavior of nonlinear systems is considered, focusing on the trade-o…
Linear Parameter-Varying Subspace Identification: A Unified Framework
P. B. Cox, R. Tóth
In this paper, we establish a unified framework for subspace identification (SID) of linear parameter-varying (LPV) systems to estimate LPV state-space (SS) models in innovation fo…