Paper Info
Title
Short note on the behavior of recurrent neural network for noisy dynamical system.
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.
Year
Venue
DocType
2019
arXiv: Neural and Evolutionary Computing
Journal
Volume
Citations 
PageRank 
abs/1904.05158
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Kyongmin Yeo1133.66