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

Daniel Han; Giuseppe Orlando; Sergei Fedotov

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arXiv · 2111.00866

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

Daniel Han·Giuseppe Orlando·Sergei Fedotov

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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.

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Daniel Han, Giuseppe Orlando, Sergei Fedotov. 2021-10-20. Identification of the nature of dynamical systems with recurrence plots and convolution neural networks: A preliminary test. https://arxiv.org/abs/2111.00866

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