Discriminating chaotic and stochastic time series using permutation entropy and artificial neural ne...
Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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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 correlations. Here we propose a new technique to relia...
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Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks
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TN_cdi_doaj_primary_oai_doaj_org_article_0920618b54bc4a88b32fead71a4318e4
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_0920618b54bc4a88b32fead71a4318e4
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-95231-z
