From fuzzy recurrence plots to scalable recurrence networks of time series

被引：20
|
作者：
Pham, Tuan D. [1 ]
机构：
[1] Linkoping Univ, Dept Biomed Engn, S-58183 Linkoping, Sweden
来源：
EPL | 2017年 / 118卷 / 02期
关键词：
COMPLEX NETWORKS; VISIBILITY GRAPH; DYNAMICS; SCALES;
D O I：
10.1209/0295-5075/118/20003
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
Recurrence networks, which are derived from recurrence plots of nonlinear time series, enable the extraction of hidden features of complex dynamical systems. Because fuzzy recurrence plots are represented as grayscale images, this paper presents a variety of texture features that can be extracted from fuzzy recurrence plots. Based on the notion of fuzzy recurrence plots, defuzzified, undirected, and unweighted recurrence networks are introduced. Network measures can be computed for defuzzified recurrence networks that are scalable to meet the demand for the network-based analysis of big data. Copyright (C) EPLA, 2017
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页数：6
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