par Antonik, Piotr 
;Gulina, Marvyn;Pauwels, Jaël ;Rontani, D.;Haelterman, Marc ;Massar, Serge
Référence (8-13 July 2018), 2018 International Joint Conference on Neural Networks (IJCNN), IEEE
Publication Publié, 2018-07
;Gulina, Marvyn;Pauwels, Jaël ;Rontani, D.;Haelterman, Marc ;Massar, Serge Référence (8-13 July 2018), 2018 International Joint Conference on Neural Networks (IJCNN), IEEE
Publication Publié, 2018-07
Publication dans des actes
| Résumé : | Reservoir computing is a machine learning approach to designing artificial neural networks. Despite the significant simplification of the training process, the performance of such systems is comparable to other digital algorithms on a series of benchmark tasks. Recent investigations have demonstrated the possibility of performing long-horizon predictions of chaotic systems using reservoir computing. In this work we show that a trained reservoir computer can reproduce sufficiently well the properties a chaotic system, hence allowing full synchronisation. We illustrate this behaviour on the Mackey-Glass and Lorenz systems. Furthermore, we show that a reservoir computer can be used to crack chaos-based cryptographic protocols and illustrate this on two encryption schemes. |
