Helpful disturbance: How nonlinear dynamics can augment edge sensor time series

Engineers at Tokyo Institute of Technology (Tokyo Tech) have demonstrated a simple computational approach for supporting the classification performance of neural networks operating on a sensor time series. The proposed technique involves feeding the recorded signal as an external forcing into an elementary nonlinear dynamical system, and providing its temporal responses to this disturbance to the neural network alongside the original data.

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