Learning unidirectional coupling using echo-state network
This work addresses a specific challenge in modeling complex dynamics for applications in reservoir computing, but it appears incremental as it applies an existing ESN method to a new coupling learning task.
The researchers tackled the problem of learning unidirectional coupling from limited time series data using an echo-state network (ESN), demonstrating that after training on a few examples, the ESN can predict response dynamics for any driver signal with the same coupling, even when the drive system is replaced.
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 $A-B$ type drive-response system in training is sufficient for the ESN to learn the coupling scheme. After training even if we replace drive system $A$ with a different system $C$, the ESN can reproduce the dynamics of response system $B$ using the dynamics of new drive system $C$ only.