Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks
Abstract 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 temp...
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Autores principales: | , , , , , |
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Formato: | article |
Lenguaje: | EN |
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Nature Portfolio
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/0920618b54bc4a88b32fead71a4318e4 |
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