IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v148y2021ics096007792100326x.html
   My bibliography  Save this article

Class-oriented techniques for reconstruction of dynamics from time series

Author

Listed:
  • Bezruchko, B.P.
  • Ponomarenko, V.I.
  • Smirnov, D.A.
  • Sysoev, I.V.
  • Prokhorov, M.D.

Abstract

Reconstruction of dynamical systems from time series is an important problem intensively studied within nonlinear dynamics and time series analysis for the last three decades. Its solution is a tool to accomplish prediction, classification, diagnostics and many other tasks. Universal approaches are quite attractive, but more specific techniques based on prior information about a system under study often appear advantageous in practice. We present an overview of the works of our team where such “class-oriented” techniques have been developed for realistic situations differing by the degree of prior knowledge: fully known structure of the dynamics equations with an accent to dealing with hidden variables and partly known structure for time-delay systems and coupled phase oscillators.

Suggested Citation

  • Bezruchko, B.P. & Ponomarenko, V.I. & Smirnov, D.A. & Sysoev, I.V. & Prokhorov, M.D., 2021. "Class-oriented techniques for reconstruction of dynamics from time series," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:chsofr:v:148:y:2021:i:c:s096007792100326x
    DOI: 10.1016/j.chaos.2021.110972
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096007792100326X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2021.110972?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. Li, Nianqiang & Pan, Wei & Yan, Lianshan & Luo, Bin & Xu, Mingfeng & Jiang, Ning & Tang, Yilong, 2011. "On joint identification of the feedback parameters for hyperchaotic systems: An optimization-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 44(4), pages 198-207.
    2. Strebel, Oliver, 2013. "A preprocessing method for parameter estimation in ordinary differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 93-104.
    3. Björn Kralemann & Matthias Frühwirth & Arkady Pikovsky & Michael Rosenblum & Thomas Kenner & Jochen Schaefer & Maximilian Moser, 2013. "In vivo cardiac phase response curve elucidates human respiratory heart rate variability," Nature Communications, Nature, vol. 4(1), pages 1-9, December.
    4. Ahmadi, Mohamadreza & Mojallali, Hamed, 2012. "Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 45(9), pages 1108-1120.
    5. Sysoeva, Marina V. & Sysoev, Ilya V. & Prokhorov, Mikhail D. & Ponomarenko, Vladimir I. & Bezruchko, Boris P., 2021. "Reconstruction of coupling structure in network of neuron-like oscillators based on a phase-locked loop," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    6. Bezruchko, Boris P. & Smirnov, Dmitry A. & Sysoev, Ilya V., 2006. "Identification of chaotic systems with hidden variables (modified Bock’s algorithm)," Chaos, Solitons & Fractals, Elsevier, vol. 29(1), pages 82-90.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ilya V. Sysoev & Danil D. Kulminskiy & Vladimir I. Ponomarenko & Mikhail D. Prokhorov, 2021. "Identification of Couplings in Adaptive Dynamical Networks of Time-Delayed Feedback Oscillators," Mathematics, MDPI, vol. 9(18), pages 1-10, September.

    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. Sysoeva, Marina V. & Sysoev, Ilya V. & Prokhorov, Mikhail D. & Ponomarenko, Vladimir I. & Bezruchko, Boris P., 2021. "Reconstruction of coupling structure in network of neuron-like oscillators based on a phase-locked loop," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Bukh, A.V. & Kashtanova, S.V. & Shepelev, I.A., 2023. "Complex error minimization algorithm with adaptive change rate," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Strebel, Oliver, 2013. "A preprocessing method for parameter estimation in ordinary differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 93-104.
    4. Ahmadi, Mohamadreza & Mojallali, Hamed, 2012. "Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 45(9), pages 1108-1120.
    5. Björn R H Blomqvist & Richard P Mann & David J T Sumpter, 2018. "Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-23, May.
    6. Mousavi, Yashar & Alfi, Alireza, 2018. "Fractional calculus-based firefly algorithm applied to parameter estimation of chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 202-215.
    7. Eddie Nijholt & Jorge Luis Ocampo-Espindola & Deniz Eroglu & István Z. Kiss & Tiago Pereira, 2022. "Emergent hypernetworks in weakly coupled oscillators," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    8. Mihailo Micev & Martin Ćalasan & Diego Oliva, 2020. "Fractional Order PID Controller Design for an AVR System Using Chaotic Yellow Saddle Goatfish Algorithm," Mathematics, MDPI, vol. 8(7), pages 1-22, July.

    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:148:y:2021:i:c:s096007792100326x. 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.