Abstract
In this study, we use deep recurrent neural networks to simulate the behaviour of dynamic chaotic attractors and to predict the next state of the system. We compare three dynamic attractors: Ueda, Pickover, and Burke-Shaw. Also, we study the ability to predict such time series simple Recurrent Neural Networks with Long Short Term Memory and Gated Recurrent Unit.
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