In this article, we focus on a topic of detecting unstable periodic orbits (UPOs) only based on the time series observed from the nonlinear dynamical system whose explicit model is completely unknown a priori. We articulate a data-driven and model-free method which connects a well-known machine learning technique, the reservoir computing, with a widely-used control strategy of nonlinear dynamical systems, the adaptive delayed feedback control. We demonstrate the advantages and effectiveness of the articulated method through detecting and controlling UPOs in representative examples and also show how those configurations of the reservoir computing in our method influence the accuracy of UPOs detection. Additionally and more interestingly, from the viewpoint of synchronization, we analytically and numerically illustrate the effectiveness of the reservoir computing in dynamical systems learning and prediction.

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