7 papers · 1 filter
Computationally Efficient Sampling-Based Algorithm for Stability Analysis of Nonlinear Systems
Péter Antal, Tamás Péni, Roland Tóth
For complex nonlinear systems, it is challenging to design algorithms that are fast, scalable, and give an accurate approximation of the stability region. This paper proposes a sam…
Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks
Yuhan Liu, Roland Tóth, Maarten Schoukens
Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical in…
Space-Filling Input Design for Nonlinear State-Space Identification
Máté Kiss, Roland Tóth, Maarten Schoukens
The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing e…
Baseline Results for Selected Nonlinear System Identification Benchmarks
Max D. Champneys, Gerben I. Beintema, Roland Tóth +2
Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting im…
On the reduction of Linear Parameter-Varying State-Space models
E. Javier Olucha, Bogoljub Terzin, Amritam Das +1
This paper presents an overview and comparative study of the state of the art in State-Order Reduction (SOR) and Scheduling Dimension Reduction (SDR) for Linear Parameter-Varying (…
Convex Equilibrium-Free Stability and Performance Analysis of Discrete-Time Nonlinear Systems
Patrick J. W. Koelewijn, Siep Weiland, Roland Tóth
This paper considers the equilibrium-free stability and performance analysis of discrete-time nonlinear systems. We consider two types of equilibrium-free notions. Namely, the univ…
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models
Sarvin Moradi, Nick Jaensson, Roland Tóth +1
In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neura…