Identification of the nature of dynamical systems with recurrence plots and convolution neural networks: A preliminary test

TL;DR

This paper introduces a hybrid method combining recurrence plots and CNNs to classify dynamical systems into chaotic, periodic, or stochastic categories with high accuracy, demonstrating the approach's effectiveness.

Contribution

It presents a novel hybrid technique using recurrence plots and CNNs for classifying dynamical systems, achieving around 90% accuracy.

Findings

Achieved approximately 90% classification accuracy.

Demonstrated the effectiveness of the hybrid approach.

Validated the method with confusion matrix and ROC analysis.

Abstract

In this study, we present a method for classifying dynamical systems using a hybrid approach involving recurrence plots and a convolution neural network (CNN). This is performed by obtaining the recurrence matrix of a time series generated from a given dynamical system and then using a CNN to classify the related dynamics observed from the recurrence matrix. We consider three broad classes of dynamics: chaotic, periodic, and stochastic. Using a relatively simple CNN structure, we are able to obtain $\sim 90\%$ accuracy in classification. The confusion matrix and receiver operating characteristic curve of classification demonstrate the strength and viability of this hybrid approach.