Short note on the behavior of recurrent neural network for noisy dynamical system

Kyongmin Yeo

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arXiv · 1904.05158

Short note on the behavior of recurrent neural network for noisy dynamical system

Kyongmin Yeo

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Abstract

The behavior of recurrent neural network for the data-driven simulation of noisy dynamical systems is studied by training a set of Long Short-Term Memory Networks (LSTM) on the Mackey-Glass time series with a wide range of noise level. It is found that, as the training noise becomes larger, LSTM learns to depend more on its autonomous dynamics than the noisy input data. As a result, LSTM trained on noisy data becomes less susceptible to the perturbation in the data, but has a longer relaxation timescale. On the other hand, when trained on noiseless data, LSTM becomes extremely sensitive to a small perturbation, but is able to adjusts to the changes in the input data.

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Kyongmin Yeo. 2019-04-05. Short note on the behavior of recurrent neural network for noisy dynamical system. https://arxiv.org/abs/1904.05158

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