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...
Guardado en:
Autores principales: | B. R. R. Boaretto, R. C. Budzinski, K. L. Rossi, T. L. Prado, S. R. Lopes, C. Masoller |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/0920618b54bc4a88b32fead71a4318e4 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks
por: Andrei Velichko, et al.
Publicado: (2021) -
Early Fault Diagnosis Technology for Bearings Based on Quantile Multiscale Permutation Entropy
por: Yufeng Long, et al.
Publicado: (2021) -
Inferring the connectivity of coupled chaotic oscillators using Kalman filtering
por: E. Forero-Ortiz, et al.
Publicado: (2021) -
Permutation entropy variation for 2007 effusive dome-forming eruption period of Kelud Volcano, Indonesia
por: Nugraheni Lusia Rita, et al.
Publicado: (2021) -
The entropy of chaotic transitions of EEG phase growth in bipolar disorder with lithium carbonate
por: Rüştü Murat Demirer, et al.
Publicado: (2021)