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		<doi>10.1142/S0218127418500086</doi>
		<issn>0218-1274</issn>
		<issn>1793-6551</issn>
		<citationkey>CostaReiZouQuiMac:2018:ReDeEn</citationkey>
		<title>Recurrence density enhanced complex networks for nonlinear time series analysis</title>
		<year>2018</year>
		<month>jan.</month>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Costa, Diego G. de B.,</author>
		<author>Reis, Barbara Maximino da Fonseca,</author>
		<author>Zou, Yong,</author>
		<author>Quiles, Marcos G.,</author>
		<author>Macau, Elbert Einstein Nehrer,</author>
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		<group>CAP-COMP-SESPG-INPE-MCTIC-GOV-BR</group>
		<group>CAP-COMP-SESPG-INPE-MCTIC-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>East China Normal University</affiliation>
		<affiliation>Universidade Federal de São Paulo (UNIFESP)</affiliation>
		<affiliation>Universidade Federal de São Paulo (UNIFESP)</affiliation>
		<electronicmailaddress></electronicmailaddress>
		<electronicmailaddress>barbara.reis@inpe.br</electronicmailaddress>
		<electronicmailaddress>yzou@phy.ecnu.edu.cn</electronicmailaddress>
		<journal>International Journal of Bifurcation and Chaos</journal>
		<volume>28</volume>
		<number>1</number>
		<pages>e1850008</pages>
		<secondarymark>A1_ENGENHARIAS_IV A2_INTERDISCIPLINAR A2_ENGENHARIAS_III A2_ENGENHARIAS_I A2_BIODIVERSIDADE B1_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B1_GEOCIÊNCIAS B1_ECONOMIA B1_CIÊNCIAS_AGRÁRIAS_I B2_CIÊNCIA_DA_COMPUTAÇÃO B4_ENSINO B4_CIÊNCIAS_BIOLÓGICAS_I B5_ASTRONOMIA_/_FÍSICA</secondarymark>
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		<keywords>Recurrence plot, recurrence networks, nonlinear time series.</keywords>
		<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.</abstract>
		<area>COMP</area>
		<language>en</language>
		<targetfile>costa_recurrence.pdf</targetfile>
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