Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks

Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to quantify nonlinear and/or high-order temporal corr...

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Detalles Bibliográficos
Autores principales: Boaretto, B. R. R., Budzinski, R. C., Rossi, K. L., Prado, T. L., Lopes, S. R., Masoller, C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8338970/
https://www.ncbi.nlm.nih.gov/pubmed/34349134
http://dx.doi.org/10.1038/s41598-021-95231-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8338970/
https://www.ncbi.nlm.nih.gov/pubmed/34349134
http://dx.doi.org/10.1038/s41598-021-95231-z

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