IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i22p3594-d1522627.html
   My bibliography  Save this article

Tsallis Entropy in MV-Algebras

Author

Listed:
  • Giuseppina Gerarda Barbieri

    (Department of Mathematics, University of Salerno, 84084 Fisciano, Italy)

  • Giacomo Lenzi

    (Department of Mathematics, University of Salerno, 84084 Fisciano, Italy)

Abstract

We deal with Tsallis entropy in MV-algebraic dynamical systems. We prove that Tsallis entropy is a submeasure and that it is invariant under isomorphisms. We also provide two examples which show that Tsallis entropy allows one to distinguish some non-isomorphic MV-dynamical systems.

Suggested Citation

  • Giuseppina Gerarda Barbieri & Giacomo Lenzi, 2024. "Tsallis Entropy in MV-Algebras," Mathematics, MDPI, vol. 12(22), pages 1-12, November.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3594-:d:1522627
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/22/3594/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/22/3594/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wilk, G. & Włodarczyk, Z., 2008. "Example of a possible interpretation of Tsallis entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(19), pages 4809-4813.
    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. Thapliyal, Richa & Taneja, H.C. & Kumar, Vikas, 2015. "Characterization results based on non-additive entropy of order statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 297-303.
    2. Xiaozhuan Gao & Yong Deng, 2019. "The generalization negation of probability distribution and its application in target recognition based on sensor fusion," International Journal of Distributed Sensor Networks, , vol. 15(5), pages 15501477198, May.
    3. Tahmasebi, S. & Longobardi, M. & Kazemi, M.R. & Alizadeh, M., 2020. "Cumulative Tsallis entropy for maximum ranked set sampling with unequal samples," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    4. Calì, Camilla & Longobardi, Maria & Ahmadi, Jafar, 2017. "Some properties of cumulative Tsallis entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 1012-1021.

    More about this item

    Keywords

    Tsallis entropy; MV-algebras;

    Statistics

    Access and download statistics

    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:gam:jmathe:v:12:y:2024:i:22:p:3594-:d:1522627. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.