14 citations · 30 across the 14 of their papers we have counts for
12 papers · 1 filter
Learning Koopman Models From Data Under General Noise Conditions
Lucian Cristian Iacob, Máté Szécsi, Gerben Izaak Beintema +2
This paper presents a novel identification approach of Koopman models of nonlinear systems with inputs under rather general noise conditions. The method uses deep state-space encod…
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…
State Derivative Normalization for Continuous-Time Deep Neural Networks
Jonas Weigand, Gerben I. Beintema, Jonas Ulmen +4
The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper nor…
Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
Gerben I. Beintema, Maarten Schoukens, Roland Tóth
Available methods for identification of stochastic dynamical systems from input-output data generally impose restricting structural assumptions on either the noise structure in the…
Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach
Rishi Ramkannan, Gerben I. Beintema, Roland Tóth +1
The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data. To achieve this, it combines the rolled-out nonlinear sta…
Computationally efficient predictive control based on ANN state-space models
Jan H. Hoekstra, Bence Cseppentő, Gerben I. Beintema +3
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting mode…
Deep Subspace Encoders for Nonlinear System Identification
Gerben I. Beintema, Maarten Schoukens, Roland Tóth
Using Artificial Neural Networks (ANN) for nonlinear system identification has proven to be a promising approach, but despite of all recent research efforts, many practical and the…
NARX Identification using Derivative-Based Regularized Neural Networks
L. H. Peeters, G. I. Beintema, M. Forgione +1
This work presents a novel regularization method for the identification of Nonlinear Autoregressive eXogenous (NARX) models. The regularization method promotes the exponential deca…
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems
Chris Verhoek, Gerben I. Beintema, Sofie Haesaert +2
The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models. Despite major r…
Deep Identification of Nonlinear Systems in Koopman Form
Lucian Cristian Iacob, Gerben Izaak Beintema, Maarten Schoukens +1
The present paper treats the identification of nonlinear dynamical systems using Koopman-based deep state-space encoders. Through this method, the usual drawback of needing to choo…
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…
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…