Recovering discrete delayed fractional equations from trajectories
Dynamical Systems
2023-09-08 v1
Abstract
We show how machine learning methods can unveil the fractional and delayed nature of discrete dynamical systems. In particular, we study the case of the fractional delayed logistic map. We show that given a trajectory, we can detect if it has some delay effect or not, and also to characterize the fractional component of the underlying generation model.
Keywords
Cite
@article{arxiv.2309.03830,
title = {Recovering discrete delayed fractional equations from trajectories},
author = {J. Alberto Conejero and Òscar Garibo-i-Orts and Carlos Lizama},
journal= {arXiv preprint arXiv:2309.03830},
year = {2023}
}
Comments
14 pages, 9 figures