Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography

Piotr Antonik, Marvyn Gulina, Jael Pauwels, Serge Massar

Résultats de recherche: Contribution à un journal/une revueArticle

Résumé

Using the machine learning approach known as reservoir computing, it is possible to train one dynamical system to emulate another. We show that such trained reservoir computers reproduce the properties of the attractor of the chaotic system sufficiently well to exhibit chaos synchronization. That is, the trained reservoir computer, weakly driven by the chaotic system, will synchronize with the chaotic system. Conversely, the chaotic system, weakly driven by a trained reservoir computer, will synchronize with the reservoir computer. We illustrate this behavior on the Mackey-Glass and Lorenz systems. We then show that trained reservoir computers can be used to crack chaos based cryptography and illustrate this on a chaos cryptosystem based on the Mackey-Glass system. We conclude by discussing why reservoir computers are so good at emulating chaotic systems.

langue originaleAnglais
Numéro d'article012215
Nombre de pages9
journalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume98
Numéro de publication1
Les DOIs
Etat de la publicationPublié - 24 juil. 2018

mots-clés

  • Ordinateur réservoir
  • Réseau de neurones récurrents
  • Réseau d'état d'écho
  • Cryptographie par chaos

Empreinte digitale Examiner les sujets de recherche de « Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography ». Ensemble, ils forment une empreinte digitale unique.

  • Contient cette citation