collaborators

6 papers

eess.SY2021

Identification of the nonlinear steering dynamics of an autonomous vehicle

G. Rödönyi, G. I. Beintema, R. Tóth +7

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital…

eess.SY2020

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…

eess.SY2020

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…

cs.LG2020

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…

eess.SY2020

On the Initialization of Nonlinear LFR Model Identification with the Best Linear Approximation

Maarten Schoukens, Roland Toth

Balancing the model complexity and the representation capability towards the process to be captured remains one of the main challenges in nonlinear system identification. One possi…

eess.SY2020

A Tree Adjoining Grammar Representation for Models Of Stochastic Dynamical Systems

Dhruv Khandelwal, Maarten Schoukens, Roland Tóth

Model structure and complexity selection remains a challenging problem in system identification, especially for parametric non-linear models. Many Evolutionary Algorithm (EA) based…