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

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.