IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v609y2023ics0378437122009165.html
   My bibliography  Save this article

Characterizing systems by multi-scale structural complexity

Author

Listed:
  • Wang, Ping
  • Gu, Changgui
  • Yang, Huijie
  • Wang, Haiying
  • Moore, Jack Murdoch

Abstract

Complexity is one of the most fundamental criteria by which people distinguish different types of systems. However, the concept of complexity remains difficult to quantify objectively. In this article, we exploit recent advances in measuring the complexity of visual images to develop a multi-scale structural complexity method for characterizing system complexity based solely on a scalar time series. Moreover, we develop a method based on scatter plots and correlation to provide visual insight into system characteristics. This method can accurately reflect the dynamical characteristics of the system as a whole. In addition, it can identify the local complexity of a system at different scales as well as indicate how the complexity of the system changes with scale. Finally, we discovered that the ratio of local complexity to total complexity varied strikingly with system type (including continuous systems, discrete systems, and empirical system), which allowed us to distinguish between them. In summary, our methods provide new insights into the multi-scale structural complexity of a system based simply on a scalar time series.

Suggested Citation

  • Wang, Ping & Gu, Changgui & Yang, Huijie & Wang, Haiying & Moore, Jack Murdoch, 2023. "Characterizing systems by multi-scale structural complexity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    • Handle: RePEc:eee:phsmap:v:609:y:2023:i:c:s0378437122009165
    DOI: 10.1016/j.physa.2022.128358
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122009165
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.128358?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. Gaetano Valenza & Luca Citi & Riccardo Barbieri, 2014. "Estimation of Instantaneous Complex Dynamics through Lyapunov Exponents: A Study on Heartbeat Dynamics," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-17, August.
    2. Yu, Jinwei & Xie, Wei & Zhong, Zhenyu & Wang, Huan, 2022. "Image encryption algorithm based on hyperchaotic system and a new DNA sequence operation," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. David H. Wolpert & William G. Macready, 1997. "Self-Dissimilarity: An Empirical Measure of Complexity," Working Papers 97-12-087, Santa Fe Institute.
    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. Xiaoqiang Zhang & Mi Liu & Xiaochang Yang, 2023. "Color Image Encryption Algorithm Based on Cross-Spiral Transformation and Zone Diffusion," Mathematics, MDPI, vol. 11(14), pages 1-28, July.
    2. Campi, Gaetano & Bianconi, Antonio, 2022. "Periodic recurrent waves of Covid-19 epidemics and vaccination campaign," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    3. Pai Liu & Shihua Zhou & Wei Qi Yan, 2022. "A 3D Cuboid Image Encryption Algorithm Based on Controlled Alternate Quantum Walk of Message Coding," Mathematics, MDPI, vol. 10(23), pages 1-26, November.

    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:phsmap:v:609:y:2023:i:c:s0378437122009165. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.