Generalized synchronization in the presence of dynamical noise and its detection via recurrent neural networks

TL;DR

This paper investigates generalized synchronization in coupled nonlinear systems under noise, extending the concept of synchronization maps to noisy scenarios and proposing neural network-based detection methods validated on synthetic and real data.

Contribution

It introduces a novel framework for detecting generalized synchronization in noisy environments using recurrent neural networks and higher-period maps.

Findings

Neural networks effectively detect synchronization in noisy data.

Synchronization maps can be extended to noisy scenarios as parameter-dependent families.

Method validated on both synthetic and real-world datasets.

Abstract

Given two unidirectionally coupled nonlinear systems, we speak of generalized synchronization when the responder \textquotedblleft follows\textquotedblright\ the driver. Mathematically, this situation is implemented by a map from the driver state space to the responder state space termed the synchronization map. In nonlinear times series analysis, the framework of the present work, the existence of the synchronization map amounts to the invertibility of the so-called cross map, which is a continuous map that exists in the reconstructed state spaces for typical time-delay embeddings. The cross map plays a central role in some techniques to detect functional dependencies between time series. In this paper, we study the changes in the \textquotedblleft noiseless scenario\textquotedblright\ just described when noise is present in the driver, a more realistic situation that we call the \textquotedblleft noisy scenario\textquotedblright . Noise will be modeled using a family of driving dynamics indexed by a finite number of parameters, which is sufficiently general for practical purposes. In this approach, it turns out that the cross and synchronization maps can be extended to the noisy scenario as families of maps that depend on the noise parameters, and only for \textquotedblleft generic\textquotedblright\ driver states in the case of the cross map. To reveal generalized synchronization in both the noiseless and noisy scenarios, we check the existence of synchronization maps of higher periods (introduced in this paper) using recurrent neural networks and predictability. The results obtained with synthetic and real world data demonstrate the capability of our method.