Analysis of chaotic dynamical systems with autoencoders.
Chaos
; 31(10): 103109, 2021 Oct.
Article
em En
| MEDLINE
| ID: mdl-34717343
ABSTRACT
We focus on chaotic dynamical systems and analyze their time series with the use of autoencoders, i.e., configurations of neural networks that map identical output to input. This analysis results in the determination of the latent space dimension of each system and thus determines the minimal number of nodes necessary to capture the essential information contained in the chaotic time series. The constructed chaotic autoencoders generate similar maximal Lyapunov exponents as the original chaotic systems and thus encompass their essential dynamical information.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Dinâmica não Linear
Idioma:
En
Revista:
Chaos
Assunto da revista:
CIENCIA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Federação Russa