Joel A. Rosenfeld

Orcid: 0000-0003-3219-448X

According to our database1, Joel A. Rosenfeld authored at least 29 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Dynamic Mode Decomposition of Control-Affine Nonlinear Systems Using Discrete Control Liouville Operators.
IEEE Control. Syst. Lett., 2024

2023
Singular Dynamic Mode Decomposition.
SIAM J. Appl. Dyn. Syst., September, 2023

Convergent Dynamic Mode Decomposition.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Modeling Partially Unknown Dynamics with Continuous Time DMD<sup>*</sup>.
Proceedings of the American Control Conference, 2023

Carleman Lifting for Nonlinear System Identification with Guaranteed Error Bounds.
Proceedings of the American Control Conference, 2023

2022
Dynamic Mode Decomposition for Continuous Time Systems with the Liouville Operator.
J. Nonlinear Sci., 2022

Fractional Order System Identification With Occupation Kernel Regression.
IEEE Control. Syst. Lett., 2022

2021
Singular Dynamic Mode Decompositions.
CoRR, 2021

An occupation kernel approach to optimal control.
CoRR, 2021

Anti-Koopmanism.
CoRR, 2021

Control Occupation Kernel Regression for Nonlinear Control-Affine Systems.
CoRR, 2021

Occupation Kernel Hilbert Spaces and the Spectral Analysis of Nonlocal Operators.
CoRR, 2021

On Occupation Kernels, Liouville Operators, and Dynamic Mode Decomposition.
Proceedings of the 2021 American Control Conference, 2021

2020
Approximate Optimal Motion Planning to Avoid Unknown Moving Avoidance Regions.
IEEE Trans. Robotics, 2020

2019
The State Following Approximation Method.
IEEE Trans. Neural Networks Learn. Syst., 2019

Invariance-Like Results for Nonautonomous Switched Systems.
IEEE Trans. Autom. Control., 2019

A mesh-free pseudospectral approach to estimating the fractional Laplacian via radial basis functions.
J. Comput. Phys., 2019

Occupation Kernels and Densely Defined Liouville Operators for System Identification.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018
Approximate Dynamic Programming: Combining Regional and Local State Following Approximations.
IEEE Trans. Neural Networks Learn. Syst., 2018

Verification for Machine Learning, Autonomy, and Neural Networks Survey.
CoRR, 2018

Reachable Set Estimation and Safety Verification for Piecewise Linear Systems with Neural Network Controllers.
Proceedings of the 2018 Annual American Control Conference, 2018

Online Approximate Optimal Path-Planner in the Presence of Mobile Avoidance Regions.
Proceedings of the 2018 Annual American Control Conference, 2018

2017
Approximating the Caputo Fractional Derivative through the Mittag-Leffler Reproducing Kernel Hilbert Space and the Kernelized Adams-Bashforth-Moulton Method.
SIAM J. Numer. Anal., 2017

2016
A Corollary for Switched Nonsmooth Systems with Applications to Switching in Adaptive Control.
CoRR, 2016

Efficient model-based reinforcement learning for approximate online optimal control.
Autom., 2016

2015
Efficient model-based reinforcement learning for approximate online optimal.
CoRR, 2015

State following (StaF) kernel functions for function approximation Part I: Theory and motivation.
Proceedings of the American Control Conference, 2015

State following (StaF) kernel functions for function approximation part II: Adaptive dynamic programming.
Proceedings of the American Control Conference, 2015

2014
Decentralized formation control with connectivity maintenance and collision avoidance under limited and intermittent sensing.
Proceedings of the American Control Conference, 2014


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