Separation of Chaotic Signals by Reservoir Computing

Abstract

We demonstrate the utility of machine learning in the separation of superimposed chaotic signals using a technique called Reservoir Computing. We assume no knowledge of the dynamical equations that produce the signals, and require only training data consisting of finite time samples of the component signals. We test our method on signals that are formed as linear combinations of signals from two Lorenz systems with different parameters. Comparing our nonlinear method with the optimal linear solution to the separation problem, the Wiener filter, we find that our method significantly outperforms the Wiener filter in all the scenarios we study. Furthermore, this difference is particularly striking when the component signals have similar frequency spectra. Indeed, our method works well when the component frequency spectra are indistinguishable - a case where a Wiener filter performs essentially no separation.

arXiv comment12 pages, 7 figures

References in corpus (2)

• Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
• On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics

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