Machine learning algorithms for predicting the amplitude of chaotic laser pulses.

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ID: 69789
2019
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Abstract
Forecasting the dynamics of chaotic systems from the analysis of their output signals is a challenging problem with applications in most fields of modern science. In this work, we use a laser model to compare the performance of several machine learning algorithms for forecasting the amplitude of upcoming emitted chaotic pulses. We simulate the dynamics of an optically injected semiconductor laser that presents a rich variety of dynamical regimes when changing the parameters. We focus on a particular dynamical regime that can show ultrahigh intensity pulses, reminiscent of rogue waves. We compare the goodness of the forecast for several popular methods in machine learning, namely, deep learning, support vector machine, nearest neighbors, and reservoir computing. Finally, we analyze how their performance for predicting the height of the next optical pulse depends on the amount of noise and the length of the time series used for training.
Reference Key
amil2019machinechaos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Amil, Pablo;Soriano, Miguel C;Masoller, Cristina;
Journal chaos (woodbury, ny)
Year 2019
DOI
10.1063/1.5120755
URL
Keywords
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