Recovering discrete delayed fractional equations from trajectories

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 fra...

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Bibliographic Details
Published inMathematical methods in the applied sciences
Main Authors Conejero, J. Alberto, Garibo‐i‐Orts, Òscar, Lizama, Carlos
Format Journal Article
LanguageEnglish
Published 26.03.2023
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Summary: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.
ISSN:0170-4214
1099-1476
DOI:10.1002/mma.9228