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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Recurrence Density Enhanced Complex Networks for Nonlinear Time Series Analysis

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Autor(es):
Costa, Diego G. de B. [1] ; Reis, Barbara M. da F. [1] ; Zou, Yong [2] ; Quiles, Marcos G. [3] ; Macau, Elbert E. N. [1, 4]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Inst Nacl Pesquisas Espaciais, Lab Associado Comp & Matemat Aplicada, Sao Jose Dos Campos, SP - Brazil
[2] East China Normal Univ, Dept Phys, Shanghai 200241 - Peoples R China
[3] Univ Fed Sao Paulo, Dept Sci & Technol, Sao Jose Dos Campos, SP - Brazil
[4] Univ Fed Sao Paulo, Inst Ciencias & Technol, Sao Jose Dos Campos, SP - Brazil
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS; v. 28, n. 1 JAN 2018.
Citações Web of Science: 1
Resumo

We introduce a new method, which is entitled Recurrence Density Enhanced Complex Network (RDE-CN), to properly analyze nonlinear time series. Our method first transforms a recurrence plot into a figure of a reduced number of points yet preserving the main and fundamental recurrence properties of the original plot. This resulting figure is then reinterpreted as a complex network, which is further characterized by network statistical measures. We illustrate the computational power of RDE-CN approach by time series by both the logistic map and experimental fluid flows, which show that our method distinguishes different dynamics sufficiently well as the traditional recurrence analysis. Therefore, the proposed methodology characterizes the recurrence matrix adequately, while using a reduced set of points from the original recurrence plots. (AU)

Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
Beneficiário:Elbert Einstein Nehrer Macau
Linha de fomento: Auxílio à Pesquisa - Temático