Complex networks approach for dynamical characterization of nonlinear systems.

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorFreitas, Vander Luis de Souza-
Autor(es): dc.creatorLacerda, Juliana Cestari-
Autor(es): dc.creatorMacau, Elbert Einstein Nehrer-
Data de aceite: dc.date.accessioned2025-08-21T15:33:56Z-
Data de disponibilização: dc.date.available2025-08-21T15:33:56Z-
Data de envio: dc.date.issued2022-12-05-
Data de envio: dc.date.issued2022-12-05-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/jspui/handle/123456789/15841-
Fonte completa do material: dc.identifierhttps://www.worldscientific.com/doi/abs/10.1142/S0218127419501888-
Fonte completa do material: dc.identifierhttps://doi.org/10.1142/S0218127419501888-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1018482-
Descrição: dc.descriptionBifurcation diagrams and Lyapunov exponents are the main tools for dynamical systems char- acterization. However, they are often computationally expensive and complex to calculate. We present two approaches for dynamical characterization of nonlinear systems via the generation of an undirected complex network that is built from their time series. Periodic windows and chaos can be detected by analyzing network statistics like average degree, density and betweenness centrality. Results are assessed in two discrete time nonlinear maps.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectNonlinear dynamics-
Palavras-chave: dc.subjectTime series analysis-
Título: dc.titleComplex networks approach for dynamical characterization of nonlinear systems.-
Aparece nas coleções:Repositório Institucional - UFOP

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