Learning unidirectional coupling using echo-state network
Abstract
Reservoir Computing has found many potential applications in the field of complex dynamics. In this article, we exploit the exceptional capability of the echo-state network (ESN) model to make it learn a unidirectional coupling scheme from only a few time series data of the system. We show that, once trained with a few example dynamics of a drive-response system, the machine is able to predict the response system's dynamics for any driver signal with the same coupling. Only a few time series data of an type drive-response system in training is sufficient for the ESN to learn the coupling scheme. After training even if we replace drive system with a different system , the ESN can reproduce the dynamics of response system using the dynamics of new drive system only.
Keywords
Cite
@article{arxiv.2303.13562,
title = {Learning unidirectional coupling using echo-state network},
author = {Swarnendu Mandal and Manish Dev Shrimali},
journal= {arXiv preprint arXiv:2303.13562},
year = {2023}
}