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Fuzzy weighted recurrence networks of time series Chock
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
2019 (English)In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 513, p. 409-417Article in journal (Refereed) Published
Abstract [en]

The concept of networks in the context of graph theory delineates a wide variety of real-life complex systems. The theory of networks finds its applications very useful in many scientific and intellectual domains. Weighted networks can characterize complex statistical graph properties, particularly where node connections are heterogeneous. A framework of fuzzy weighted recurrence networks of time series is presented in this letter. Popular graph measures including the average clustering coefficient and characteristic path length of fuzzy weighted recurrence networks are shown to be more robust than those of unweighted recurrence networks derived from binary recurrence plots. (C) 2018 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2019. Vol. 513, p. 409-417
Keywords [en]
Time series; Nonlinear dynamics; Fuzzy recurrence plots; Fuzzy weighted recurrence networks
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Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-152587DOI: 10.1016/j.physa.2018.09.035ISI: 000448496200037OAI: oai:DiVA.org:liu-152587DiVA, id: diva2:1262148
Available from: 2018-11-09 Created: 2018-11-09 Last updated: 2018-12-04

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The full text will be freely available from 2020-09-08 10:45
Available from 2020-09-08 10:45

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Pham, Tuan
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