IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v180y2024ics0960077924000559.html
   My bibliography  Save this article

First integrals can explain coexistence of attractors, multistability, and loss of ideality in circuits with memristors

Author

Listed:
  • Innocenti, Giacomo
  • Tesi, Alberto
  • Di Marco, Mauro
  • Forti, Mauro

Abstract

In this paper a systematic procedure to compute the first integrals of the dynamics of a circuit with an ideal memristor is presented. In this perspective, the state space results in a layered structure of manifolds generated by first integrals, which are associated, via the choice of the initial conditions, to different exhibited behaviors. This feature turns out to be a powerful investigation tool, and it can be used to disclose the coexistence of attractors and the so called “extreme multistability,” which are typical of the circuits with ideal memristors. The first integrals can also be exploited to study the energetic behavior of both the circuit and of the memristor itself. How to extend these results to the other ideal memelements and to more complex circuit configurations is shortly mentioned. Moreover, a class of ideal memristive devices capable of inducing the same first integrals layered in the state space is introduced. Finally, a mechanism for the loss of the ideality is conceived in terms of spoiling the first integrals structure, which makes it possible to develop a non-ideal memristive model. Notably, this latter can be interpreted as an ideal memristive device subject to a dynamic nonlinear feedback, thus highlighting that the non-ideal model is still affected by the first integrals influence, and justifying the importance of studying the ideal devices in order to understand the non-ideal ones.

Suggested Citation

  • Innocenti, Giacomo & Tesi, Alberto & Di Marco, Mauro & Forti, Mauro, 2024. "First integrals can explain coexistence of attractors, multistability, and loss of ideality in circuits with memristors," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:chsofr:v:180:y:2024:i:c:s0960077924000559
    DOI: 10.1016/j.chaos.2024.114504
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924000559
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2024.114504?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. Peng, Yuexi & Sun, Kehui & He, Shaobo, 2020. "A discrete memristor model and its application in Hénon map," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    2. Dmitri B. Strukov & Gregory S. Snider & Duncan R. Stewart & R. Stanley Williams, 2008. "The missing memristor found," Nature, Nature, vol. 453(7191), pages 80-83, May.
    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. 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).
    2. Zhong, Huiyan & Li, Guodong & Xu, Xiangliang, 2022. "A generic voltage-controlled discrete memristor model and its application in chaotic map," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    3. 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).
    4. Wang, Qiao & Tian, Zean & Wu, Xianming & Li, Kunshuai & Sang, Haiwei & Yu, Xiong, 2024. "A 5D super-extreme-multistability hyperchaotic map based on parallel-cascaded memristors," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    5. Zhang, Shaohua & Zhang, Hongli & Wang, Cong, 2023. "Memristor initial-boosted extreme multistability in the novel dual-memristor hyperchaotic maps," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    6. 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).
    7. Min, Fuhong & Zhang, Wen & Ji, Ziyi & Zhang, Lei, 2021. "Switching dynamics of a non-autonomous FitzHugh-Nagumo circuit with piecewise-linear flux-controlled memristor," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    8. Feng, Liang & Hu, Cheng & Yu, Juan & Jiang, Haijun & Wen, Shiping, 2021. "Fixed-time Synchronization of Coupled Memristive Complex-valued Neural Networks," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    9. Hu, Yongbing & Li, Qian & Ding, Dawei & Jiang, Li & Yang, Zongli & Zhang, Hongwei & Zhang, Zhixin, 2021. "Multiple coexisting analysis of a fractional-order coupled memristive system and its application in image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    10. Yan, Dengwei & Wang, Lidan & Duan, Shukai & Chen, Jiaojiao & Chen, Jiahao, 2021. "Chaotic Attractors Generated by a Memristor-Based Chaotic System and Julia Fractal," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    11. Luo, Mengzhuo & Cheng, Jun & Liu, Xinzhi & Zhong, Shouming, 2019. "An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control," Applied Mathematics and Computation, Elsevier, vol. 344, pages 163-182.
    12. Liu, Shuxin & Yu, Yongguang & Zhang, Shuo & Zhang, Yuting, 2018. "Robust stability of fractional-order memristor-based Hopfield neural networks with parameter disturbances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 845-854.
    13. Zhang, Ge & Ma, Jun & Alsaedi, Ahmed & Ahmad, Bashir & Alzahrani, Faris, 2018. "Dynamical behavior and application in Josephson Junction coupled by memristor," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 290-299.
    14. Chen, Qun & Li, Bo & Yin, Wei & Jiang, Xiaowei & Chen, Xiangyong, 2023. "Bifurcation, chaos and fixed-time synchronization of memristor cellular neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    15. Stavrinides, Stavros G. & Hanias, Michael P. & Gonzalez, Mireia B. & Campabadal, Francesca & Contoyiannis, Yiannis & Potirakis, Stelios M. & Al Chawa, Mohamad Moner & de Benito, Carol & Tetzlaff, Rona, 2022. "On the chaotic nature of random telegraph noise in unipolar RRAM memristor devices," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    16. Li, Liangchen & Xu, Rui & Lin, Jiazhe, 2020. "Lagrange stability for uncertain memristive neural networks with Lévy noise and leakage delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    17. Rohit Abraham John & Yiğit Demirağ & Yevhen Shynkarenko & Yuliia Berezovska & Natacha Ohannessian & Melika Payvand & Peng Zeng & Maryna I. Bodnarchuk & Frank Krumeich & Gökhan Kara & Ivan Shorubalko &, 2022. "Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    18. Sakthivel, R. & Anbuvithya, R. & Mathiyalagan, K. & Ma, Yong-Ki & Prakash, P., 2016. "Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions," Applied Mathematics and Computation, Elsevier, vol. 275(C), pages 213-228.
    19. Liying Xu & Jiadi Zhu & Bing Chen & Zhen Yang & Keqin Liu & Bingjie Dang & Teng Zhang & Yuchao Yang & Ru Huang, 2022. "A distributed nanocluster based multi-agent evolutionary network," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    20. Hua, Wentao & Wang, Yantao & Liu, Chunyan, 2024. "New method for global exponential synchronization of multi-link memristive neural networks with three kinds of time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 471(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:180:y:2024:i:c:s0960077924000559. 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.