IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v179y2024ics0960077923013085.html
   My bibliography  Save this article

Coexisting hollow chaotic attractors within a steep parameter interval

Author

Listed:
  • Li, Yongxin
  • Li, Chunbiao
  • Zhong, Qing
  • Zhao, Yibo
  • Yang, Yong

Abstract

By transforming the past states of a variable to another variable, and returning the evolvement along with various functions of self-feedback, a unique gallery of chaotic maps with coexisting hollow attractors is proposed. Strikingly, these peculiar attractors stand on steep parameter intervals, some of which could only happen when the parameter accurately approaches the integer 1 instead of 1. Some maps also have direct amplitude controllers, which regulate the dynamics together with the initial condition leaving unpredicted complexity. The nested hollow attractors are arranged in phase space with great distribution diversity in the form of homogeneous and heterogeneous multistability. CH32-based digital circuit experiments show agreement with numerical simulation and theoretical prediction. Furthermore, the application in secure optical communication is explored accordingly proving higher performance in the steep parameter interval.

Suggested Citation

  • Li, Yongxin & Li, Chunbiao & Zhong, Qing & Zhao, Yibo & Yang, Yong, 2024. "Coexisting hollow chaotic attractors within a steep parameter interval," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    • Handle: RePEc:eee:chsofr:v:179:y:2024:i:c:s0960077923013085
    DOI: 10.1016/j.chaos.2023.114406
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923013085
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2023.114406?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. Lin, Hairong & Wang, Chunhua & Du, Sichun & Yao, Wei & Sun, Yichuang, 2023. "A family of memristive multibutterfly chaotic systems with multidirectional initial-based offset boosting," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    2. Lai, Qiang & Yang, Liang & Liu, Yuan, 2022. "Design and realization of discrete memristive hyperchaotic map with application in image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    3. Peng, Yuexi & Sun, Kehui & He, Shaobo, 2020. "A discrete memristor model and its application in Hénon map," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    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. Li, Yongxin & Li, Chunbiao & Li, Yaning & Moroz, Irene & Yang, Yong, 2024. "A joint image encryption based on a memristive Rulkov neuron with controllable multistability and compressive sensing," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    2. Yang, Jie & Li, Chunbiao & Zhang, Qian & Zhang, Xin & Wu, Zhihao & Zhong, Haidong & Liu, Peiqiao & Liu, Zuohua & Tao, Changyuan & Huang, Keyu & Li, Jiaxing & Zheng, Guocan, 2024. "A memristive hyperchaotic oscillator with complete control and its application in the electrolysis of manganese," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    3. Bao, Bocheng & Chen, Liuhui & Bao, Han & Chen, Mo & Xu, Quan, 2024. "Bifurcations to bursting oscillations in memristor-based FitzHugh-Nagumo circuit," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. Huang, Keyu & Li, Chunbiao & Cen, Xiaoliang & Chen, Guanrong, 2024. "Constructing chaotic oscillators with memory components," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).

    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. Cao, Hongli & Wang, Yu & Banerjee, Santo & Cao, Yinghong & Mou, Jun, 2024. "A discrete Chialvo–Rulkov neuron network coupled with a novel memristor model: Design, Dynamical analysis, DSP implementation and its application," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    2. Fan, Zhenyi & Zhang, Chenkai & Wang, Yiming & Du, Baoxiang, 2023. "Construction, dynamic analysis and DSP implementation of a novel 3D discrete memristive hyperchaotic map," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    3. Dlamini, A. & Doungmo Goufo, E.F., 2023. "Generation of self-similarity in a chaotic system of attractors with many scrolls and their circuit’s implementation," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    4. Huang, Keyu & Li, Chunbiao & Cen, Xiaoliang & Chen, Guanrong, 2024. "Constructing chaotic oscillators with memory components," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    5. SaberiKamarposhti, Morteza & Ghorbani, Amirabbas & Yadollahi, Mehdi, 2024. "A comprehensive survey on image encryption: Taxonomy, challenges, and future directions," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    6. Zhang, Jie & Zuo, Jiangang & Wang, Meng & Guo, Yan & Xie, Qinggang & Hou, Jinyou, 2024. "Design and application of multiscroll chaotic attractors based on a novel multi-segmented memristor," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    7. Deng, Yue & Li, Yuxia, 2021. "Bifurcation and bursting oscillations in 2D non-autonomous discrete memristor-based hyperchaotic map," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    8. Wu, Huagan & Gu, Jinxiang & Guo, Yixuan & Chen, Mo & Xu, Quan, 2024. "Biphasic action potentials in an individual cellular neural network cell," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    9. Hairong Lin & Chunhua Wang & Fei Yu & Jingru Sun & Sichun Du & Zekun Deng & Quanli Deng, 2023. "A Review of Chaotic Systems Based on Memristive Hopfield Neural Networks," Mathematics, MDPI, vol. 11(6), pages 1-18, March.
    10. Qingye Huang & Linqing Huang & Shuting Cai & Xiaoming Xiong & Hui Zhang, 2023. "On a Symmetric Image Cryptosystem Based on a Novel One-Dimensional Chaotic System and Banyan Network," Mathematics, MDPI, vol. 11(21), pages 1-21, October.
    11. Lai, Qiang & Hua, Hanqiang & Zhao, Xiao-Wen & Erkan, Uǧur & Toktas, Abdurrahim, 2023. "Image encryption using fission diffusion process and a new hyperchaotic map," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    12. Jayaraman Venkatesh & Alexander N. Pchelintsev & Anitha Karthikeyan & Fatemeh Parastesh & Sajad Jafari, 2023. "A Fractional-Order Memristive Two-Neuron-Based Hopfield Neuron Network: Dynamical Analysis and Application for Image Encryption," Mathematics, MDPI, vol. 11(21), pages 1-17, October.
    13. Ren, Lujie & Mou, Jun & Banerjee, Santo & Zhang, Yushu, 2023. "A hyperchaotic map with a new discrete memristor model: Design, dynamical analysis, implementation and application," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    14. Lai, Qiang & Chen, Zhijie, 2023. "Grid-scroll memristive chaotic system with application to image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    15. Fei Yu & Wuxiong Zhang & Xiaoli Xiao & Wei Yao & Shuo Cai & Jin Zhang & Chunhua Wang & Yi Li, 2023. "Dynamic Analysis and FPGA Implementation of a New, Simple 5D Memristive Hyperchaotic Sprott-C System," Mathematics, MDPI, vol. 11(3), pages 1-15, January.
    16. Zhao, Zhigao & Chen, Fei & He, Xianghui & Lan, Pengfei & Chen, Diyi & Yin, Xiuxing & Yang, Jiandong, 2024. "A universal hydraulic-mechanical diagnostic framework based on feature extraction of abnormal on-field measurements: Application in micro pumped storage system," Applied Energy, Elsevier, vol. 357(C).
    17. Lai, Qiang & Hu, Genwen & Erkan, Uǧur & Toktas, Abdurrahim, 2023. "High-efficiency medical image encryption method based on 2D Logistic-Gaussian hyperchaotic map," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    18. Jia, Junen & Wang, Chunni & Zhang, Xiaofeng & Zhu, Zhigang, 2024. "Energy and self-adaption in a memristive map neuron," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    19. Lu, Yang & Gong, Mengxin & Gan, Zhihua & Chai, Xiuli & Cao, Lvchen & Wang, Binjie, 2023. "Exploiting one-dimensional improved Chebyshev chaotic system and partitioned diffusion based on the divide-and-conquer principle for 3D medical model encryption," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    20. Borges, Vinícius S. & Eisencraft, Marcio, 2022. "A filtered Hénon map," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).

    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:179:y:2024:i:c:s0960077923013085. 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.