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@Article{CostaReiZouQuiMac:2018:ReDeEn,
               author = "Costa, Diego G. de B. and Reis, Barbara Maximino da Fonseca and 
                         Zou, Yong and Quiles, Marcos G. and Macau, Elbert Einstein 
                         Nehrer",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {East China Normal 
                         University} and {Universidade Federal de S{\~a}o Paulo (UNIFESP)} 
                         and {Universidade Federal de S{\~a}o Paulo (UNIFESP)}",
                title = "Recurrence density enhanced complex networks for nonlinear time 
                         series analysis",
              journal = "International Journal of Bifurcation and Chaos",
                 year = "2018",
               volume = "28",
               number = "1",
                pages = "e1850008",
                month = "jan.",
             keywords = "Recurrence plot, recurrence networks, nonlinear time series.",
             abstract = "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.",
                  doi = "10.1142/S0218127418500086",
                  url = "http://dx.doi.org/10.1142/S0218127418500086",
                 issn = "0218-1274 and 1793-6551",
             language = "en",
           targetfile = "costa_recurrence.pdf",
        urlaccessdate = "26 nov. 2020"
}


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