Fuzzy Recurrence Networks

Tuan D. Pham ()
Additional contact information
Tuan D. Pham: Prince Mohammad Bin Fahd University, The Center for Artificial Intelligence

Chapter Chapter 5 in Fuzzy Recurrence Plots and Networks with Applications in Biomedicine, 2020, pp 57-79 from Springer

Abstract: Abstract Fuzzy recurrence networksRecurrence networks can beFuzzy recurrence network constructed fromFuzzy recurrence plots fuzzy recurrence plotsRecurrence plots of nonlinear time series to extract latent features of complex dynamical systemsDynamical systems. Defuzzified, undirected, unweighted, and weighted networks of fuzzy recurrences are described in this chapter. Fuzzy recurrenceFuzzy recurrence network networksRecurrence networks are scalable to meet the demand for the network-based analysis of big data and useful for the handling of very long time seriesTime series to reduce the computational requirements in terms of computing time and memory storage. The formulation ofWeighted fuzzy recurrence networks weighted fuzzy recurrence networksRecurrence networks of multichannel dataMultichannel data is also presented in this chapter.

Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-37530-0_5

Ordering information: This item can be ordered from
http://www.springer.com/9783030375300

DOI: 10.1007/978-3-030-37530-0_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().