Maarten Schoukens

Paul M. J. Van den Hof

CoRR, 2024

Space-Filling Input Design for Nonlinear State-Space Identification.

[BibT_eX]

[DOI]

Máté Kiss

CoRR, 2024

Baseline Results for Selected Nonlinear System Identification Benchmarks.

[BibT_eX]

[DOI]

Max D. Champneys

Timothy J. Rogers

CoRR, 2024

Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks.

[BibT_eX]

[DOI]

Yuhan Liu

CoRR, 2024

Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees.

[BibT_eX]

[DOI]

CoRR, 2024

Learning-based model augmentation with LFRs.

[BibT_eX]

[DOI]

CoRR, 2024

State Derivative Normalization for Continuous-Time Deep Neural Networks.

[BibT_eX]

[DOI]

Jonas Weigand

CoRR, 2024

Koopman form of nonlinear systems with inputs.

[BibT_eX]

[DOI]

Autom., 2024

Meta-state-space learning: An identification approach for stochastic dynamical systems.

[BibT_eX]

[DOI]

Autom., 2024

Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Medical Measurements and Applications, 2024

2023

Deep subspace encoders for nonlinear system identification.

[BibT_eX]

[DOI]

Autom., October, 2023

Automated multi-objective system identification using grammar-based genetic programming.

[BibT_eX]

[DOI]

Autom., August, 2023

Data-Driven Model-Reference Control With Closed-Loop Stability: The Output-Feedback Case.

[BibT_eX]

[DOI]

IEEE Control. Syst. Lett., 2023

Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models.

[BibT_eX]

[DOI]

CoRR, 2023

Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach.

[BibT_eX]

[DOI]

Rishi Ramkannan

CoRR, 2023

Finite Dimensional Koopman Form of Polynomial Nonlinear Systems.

[BibT_eX]

[DOI]

CoRR, 2023

Continuous-time identification of dynamic state-space models by deep subspace encoding.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Nonlinear Data-Driven Predictive Control Using Deep Subspace Prediction Networks.

[BibT_eX]

[DOI]

Mircea Lazar

Mihai-Serban Popescu

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Computationally Efficient Predictive Control Based on ANN State-Space Models.

[BibT_eX]

[DOI]

Jan H. Hoekstra

Bence Cseppento

Zsolt Kollár

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022

A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies.

[BibT_eX]

[DOI]

David van de Sanden

Mauro Salazar

CoRR, 2022

Optimal Synthesis of LTI Koopman Models for Nonlinear Systems with Inputs.

[BibT_eX]

[DOI]

CoRR, 2022

Deep subspace encoders for continuous-time state-space identification.

[BibT_eX]

[DOI]

CoRR, 2022

Deep-Learning-Based Identification of LPV Models for Nonlinear Systems.

[BibT_eX]

[DOI]

Chris Verhoek

Sofie Haesaert

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Message Passing-based System Identification for NARMAX Models.

[BibT_eX]

[DOI]

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

NARX Identification using Derivative-Based Regularized Neural Networks.

[BibT_eX]

[DOI]

L. H. Peeters

M. Forgione

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Variational message passing for online polynomial NARMAX identification.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2022

2021

Identification of the nonlinear steering dynamics of an autonomous vehicle.

[BibT_eX]

[DOI]

Gábor Rödönyi

Alexandros Soumelidis

József Bokor

CoRR, 2021

Feedback identification of conductance-based models.

[BibT_eX]

[DOI]

Thiago B. Burghi

Rodolphe Sepulchre

Autom., 2021

Nonlinear state-space identification using deep encoder networks.

[BibT_eX]

[DOI]

Gerben Beintema

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Improved Initialization of State-Space Artificial Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 2021 European Control Conference, 2021

Deep Identification of Nonlinear Systems in Koopman Form.

[BibT_eX]

[DOI]

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

Best Linear Approximation of Nonlinear Continuous-Time Systems Subject to Process Noise and Operating in Feedback.

[BibT_eX]

[DOI]

John Lataire

IEEE Trans. Instrum. Meas., 2020

Extending the Best Linear Approximation Framework to the Process Noise Case.

[BibT_eX]

[DOI]

Tadeusz P. Dobrowiecki

IEEE Trans. Autom. Control., 2020

Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming.

[BibT_eX]

[DOI]

Stefan-Cristian Nechita

CoRR, 2020

Non-linear State-space Model Identification from Video Data using Deep Encoders.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

A Tree Adjoining Grammar representation for models of stochastic dynamical systems.

[BibT_eX]

[DOI]

Autom., 2020

System identification of biophysical neuronal models.

[BibT_eX]

[DOI]

Thiago B. Burghi

Rodolphe Sepulchre

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019

Sampled-data adaptive observer for state-affine systems with uncertain output equation.

[BibT_eX]

[DOI]

Autom., 2019

Grammar-based Representation and Identification of Dynamical Systems.

[BibT_eX]

[DOI]

Proceedings of the 17th European Control Conference, 2019

Feedback for nonlinear system identification.

[BibT_eX]

[DOI]

Thiago B. Burghi

Rodolphe Sepulchre

Proceedings of the 17th European Control Conference, 2019

Data-driven Modelling of Dynamical Systems Using Tree Adjoining Grammar and Genetic Programming.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2019

2018

Linear Parameter Varying Representation of a class of MIMO Nonlinear Systems.

[BibT_eX]

[DOI]

CoRR, 2018

From Nonlinear Identification to Linear Parameter Varying Models: Benchmark Examples.

[BibT_eX]

[DOI]

CoRR, 2018

Comparison of several data-driven nonlinear system identification methods on a simplified glucoregulatory system example.

[BibT_eX]

[DOI]

Anna Marconato

Widanalage Dhammika Widanage

Amjad Abu-Rmileh

CoRR, 2018

Fast Identification of Wiener-Hammerstein Systems using Discrete Optimization.

[BibT_eX]

[DOI]

CoRR, 2018

On the Simulation of Polynomial NARMAX Models.

[BibT_eX]

[DOI]

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017

Structure Detection of Wiener-Hammerstein Systems With Process Noise.

[BibT_eX]

[DOI]

Erliang Zhang

IEEE Trans. Instrum. Meas., 2017

Identification of block-oriented nonlinear systems starting from linear approximations: A survey.

[BibT_eX]

[DOI]

Autom., 2017

2016

Identification of Nonlinear Block-Oriented Systems starting from Linear Approximations: A Survey.

[BibT_eX]

[DOI]

CoRR, 2016

Obtaining the Pre-Inverse of a Power Amplifier using Iterative Learning Control.

[BibT_eX]

[DOI]

Jules Hammenecker

Adam Cooman

CoRR, 2016

Filter-based regularisation for impulse response modelling.

[BibT_eX]

[DOI]

Anna Marconato

CoRR, 2016

Filter interpretation of regularized impulse response modeling.

[BibT_eX]

[DOI]

Anna Marconato

Proceedings of the 15th European Control Conference, 2016

2015

Initial estimates for Wiener-Hammerstein models using phase-coupled multisines.

[BibT_eX]

[DOI]

Autom., 2015

Structure discrimination in block-oriented models using linear approximations: A theoretic framework.

[BibT_eX]

[DOI]

Autom., 2015

Parametric identification of parallel Wiener-Hammerstein systems.

[BibT_eX]

[DOI]

Autom., 2015

Decoupling static nonlinearities in a parallel Wiener-Hammerstein system: A first-order approach.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2015

2014

Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation.

[BibT_eX]

[DOI]

Autom., 2014

System identification in a real world.

[BibT_eX]

[DOI]

Proceedings of the IEEE 13th International Workshop on Advanced Motion Control, 2014

2013

An identification algorithm for parallel Wiener-Hammerstein systems.

[BibT_eX]

[DOI]

Gerd Vandersteen

Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Study of the effective number of parameters in nonlinear identification benchmarks.

[BibT_eX]

[DOI]

Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Combining the Best Linear Approximation and Dimension Reduction to Identify the Linear Blocks of Parallel Wiener Systems.

[BibT_eX]

[DOI]

Christian Lyzell

Martin Enqvist

Proceedings of the 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, 2013

2012

Parametric Identification of Parallel Wiener Systems.

[BibT_eX]

[DOI]

IEEE Trans. Instrum. Meas., 2012

Cross-term Elimination in Parallel Wiener Systems Using a Linear Input Transformation.

[BibT_eX]

[DOI]

IEEE Trans. Instrum. Meas., 2012

2011

Parametric Identification of Parallel Hammerstein Systems.

[BibT_eX]

[DOI]

IEEE Trans. Instrum. Meas., 2011

Parametric MIMO parallel Wiener identification.

[BibT_eX]

[DOI]