IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v164y2022ics0960077922008359.html
   My bibliography  Save this article

Indicating the synchronization bifurcation points using the early warning signals in two case studies: Continuous and explosive synchronization

Author

Listed:
  • Karimi Rahjerdi, Bahareh
  • Ramamoorthy, Ramesh
  • Nazarimehr, Fahimeh
  • Rajagopal, Karthikeyan
  • Jafari, Sajad

Abstract

Synchronization is one of the interesting collective behaviors of oscillators. It refers to a phenomenon in which some dynamically connected systems behave the same. In dynamic systems, as the bifurcation points of the system approach, the system slows down and returns to its steady-state later with a slight disturbance. The system's slowness before bifurcation points is called critical slowing down, which can be measured using early warning (EW) indicators. This paper considers two dynamic systems: the Kuramoto model and the Rössler system. Two networks are considered to show continuous and explosive synchronization in each system. Then the synchronization bifurcation point (SBP) of each case is indicated using the EW signals calculated using the time series of synchronization measures. EW signals measure the slowness in the synchronous benchmark time series. Two EW signals, skewness and kurtosis, are applied. The results show that the SBPs in various cases and systems can be predicted using the EW signals.

Suggested Citation

  • Karimi Rahjerdi, Bahareh & Ramamoorthy, Ramesh & Nazarimehr, Fahimeh & Rajagopal, Karthikeyan & Jafari, Sajad, 2022. "Indicating the synchronization bifurcation points using the early warning signals in two case studies: Continuous and explosive synchronization," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    • Handle: RePEc:eee:chsofr:v:164:y:2022:i:c:s0960077922008359
    DOI: 10.1016/j.chaos.2022.112656
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922008359
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112656?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vasilis Dakos & Stephen R Carpenter & William A Brock & Aaron M Ellison & Vishwesha Guttal & Anthony R Ives & Sonia Kéfi & Valerie Livina & David A Seekell & Egbert H van Nes & Marten Scheffer, 2012. "Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-20, July.
    2. Dan Tian & Qura-Tul Ain & Naveed Anjum & Chun-Hui He & Bin Cheng, 2021. "Fractal N/Mems: From Pull-In Instability To Pull-In Stability," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 29(02), pages 1-8, March.
    3. Robert M. May & Simon A. Levin & George Sugihara, 2008. "Ecology for bankers," Nature, Nature, vol. 451(7181), pages 893-894, February.
    4. Sajjadi, Samaneh Sadat & Baleanu, Dumitru & Jajarmi, Amin & Pirouz, Hassan Mohammadi, 2020. "A new adaptive synchronization and hyperchaos control of a biological snap oscillator," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    5. Dai, X. & Kovalenko, K. & Molodyk, M. & Wang, Z. & Li, X. & Musatov, D. & Raigorodskii, A.M. & Alfaro-Bittner, K. & Cooper, G.D. & Bianconi, G. & Boccaletti, S., 2021. "D-dimensional oscillators in simplicial structures: Odd and even dimensions display different synchronization scenarios," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    6. Marten Scheffer & Jordi Bascompte & William A. Brock & Victor Brovkin & Stephen R. Carpenter & Vasilis Dakos & Hermann Held & Egbert H. van Nes & Max Rietkerk & George Sugihara, 2009. "Early-warning signals for critical transitions," Nature, Nature, vol. 461(7260), pages 53-59, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Georg Jäger & Manfred Füllsack, 2019. "Systematically false positives in early warning signal analysis," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-14, February.
    2. Georg Jäger & Christian Hofer & Marie Kapeller & Manfred Füllsack, 2017. "Hidden early-warning signals in scale-free networks," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-14, December.
    3. Richter, Andries & Dakos, Vasilis, 2015. "Profit fluctuations signal eroding resilience of natural resources," Ecological Economics, Elsevier, vol. 117(C), pages 12-21.
    4. James J Elser & Timothy J Elser & Stephen R Carpenter & William A Brock, 2014. "Regime Shift in Fertilizer Commodities Indicates More Turbulence Ahead for Food Security," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-7, May.
    5. Fushing, Hsieh & Jordà, Òscar & Beisner, Brianne & McCowan, Brenda, 2014. "Computing systemic risk using multiple behavioral and keystone networks: The emergence of a crisis in primate societies and banks," International Journal of Forecasting, Elsevier, vol. 30(3), pages 797-806.
    6. Martin Lindegren & Vasilis Dakos & Joachim P Gröger & Anna Gårdmark & Georgs Kornilovs & Saskia A Otto & Christian Möllmann, 2012. "Early Detection of Ecosystem Regime Shifts: A Multiple Method Evaluation for Management Application," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    7. Zeng, Chunhua & Wang, Hua, 2012. "Noise and large time delay: Accelerated catastrophic regime shifts in ecosystems," Ecological Modelling, Elsevier, vol. 233(C), pages 52-58.
    8. Katherine A Spielmann & Matthew A Peeples & Donna M Glowacki & Andrew Dugmore, 2016. "Early Warning Signals of Social Transformation: A Case Study from the US Southwest," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-18, October.
    9. Manfred Füllsack & Daniel Reisinger & Marie Kapeller & Georg Jäger, 2022. "Early warning signals from the periphery," Journal of Computational Social Science, Springer, vol. 5(1), pages 665-685, May.
    10. Andrew R. Tilman & Elisabeth H. Krueger & Lisa C. McManus & James R. Watson, 2023. "Maintaining human wellbeing as socio-environmental systems undergo regime shifts," Papers 2309.04578, arXiv.org.
    11. Haoyu Wen & Massimo Pica Ciamarra & Siew Ann Cheong, 2018. "How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-22, March.
    12. William A Brock & Stephen R Carpenter, 2012. "Early Warnings of Regime Shift When the Ecosystem Structure Is Unknown," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-10, September.
    13. Tatiana Baumuratova & Simona Dobre & Thierry Bastogne & Thomas Sauter, 2013. "Switch of Sensitivity Dynamics Revealed with DyGloSA Toolbox for Dynamical Global Sensitivity Analysis as an Early Warning for System's Critical Transition," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    14. Bichler, Shimshon & Nitzan, Jonathan, 2010. "Systemic Fear, Modern Finance and the Future of Capitalism," EconStor Preprints 157830, ZBW - Leibniz Information Centre for Economics.
    15. Krishnadas M. & K. P. Harikrishnan & G. Ambika, 2022. "Recurrence measures and transitions in stock market dynamics," Papers 2208.03456, arXiv.org.
    16. Irina Alchinova & Mikhail Karganov, 2021. "Physiological Balance of the Body: Theory, Algorithms, and Results," Mathematics, MDPI, vol. 9(3), pages 1-8, January.
    17. Alessandro Spelta, 2016. "Stock prices prediction via tensor decomposition and links forecast," DISCE - Working Papers del Dipartimento di Economia e Finanza def041, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    18. Kiran D’Souza & Bogdan I Epureanu & Mercedes Pascual, 2015. "Forecasting Bifurcations from Large Perturbation Recoveries in Feedback Ecosystems," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-19, September.
    19. Riddhi Singh & Julianne D Quinn & Patrick M Reed & Klaus Keller, 2018. "Skill (or lack thereof) of data-model fusion techniques to provide an early warning signal for an approaching tipping point," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-21, February.
    20. Yang, Anji & Wang, Hao & Yuan, Sanling, 2023. "Tipping time in a stochastic Leslie predator–prey model," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:164:y:2022:i:c:s0960077922008359. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.