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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
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 |