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Title

Machine learning algorithms for predicting the amplitude of chaotic laser pulses

AuthorsAmil, Pablo; Soriano, Miguel C. ; Masoller, Cristina
Issue Date12-Nov-2019
PublisherAmerican Institute of Physics
CitationChaos 29: 113111 (2019)
AbstractForecasting 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.
Publisher version (URL)http://doi.org/10.1063/1.5120755
URIhttp://hdl.handle.net/10261/204821
DOI10.1063/1.5120755
Identifiersdoi: 10.1063/1.5120755
issn: 1054-1500
e-issn: 1089-7682
Appears in Collections:(IFISC) Artículos
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