IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v150y2021ics0960077921004641.html
   My bibliography  Save this article

Projective synchronization of memristive multidirectional associative memory neural networks via self-triggered impulsive control and its application to image protection

Author

Listed:
  • Wang, Weiping
  • Sun, Yue
  • Yuan, Manman
  • Wang, Zhen
  • Cheng, Jun
  • Fan, Denggui
  • Kurths, Jürgen
  • Luo, Xiong
  • Wang, Chunyang

Abstract

This paper presents a new synchronization criterion with a hybrid control approach for multidirectional associative memory neural networks based on memristor (MMAMNNs). That is, the method of impulsive and feedback control combing with the (event) self-triggered mechanism is adopted. However some projective synchronization errors based on state related parameters of MMAMNNs will be affected by the diverse initial conditions. Thus, the new criterion is supported by establishing a novel Lyapunov function combined with the features of such diverse parameters and systems. A collaborative proposed method is designed to make the error of such system converging to zero. Then, the Zeno-behavior is testified to disappear from the proposed programs. Finally, some examples demonstrate the validity of the proposed method and to show its potential application in image protection.

Suggested Citation

  • Wang, Weiping & Sun, Yue & Yuan, Manman & Wang, Zhen & Cheng, Jun & Fan, Denggui & Kurths, Jürgen & Luo, Xiong & Wang, Chunyang, 2021. "Projective synchronization of memristive multidirectional associative memory neural networks via self-triggered impulsive control and its application to image protection," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:chsofr:v:150:y:2021:i:c:s0960077921004641
    DOI: 10.1016/j.chaos.2021.111110
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077921004641
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2021.111110?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. Wenjiao Sun & Guojian Ren & Yongguang Yu & Xudong Hai, 2020. "Global Synchronization of Reaction-Diffusion Fractional-Order Memristive Neural Networks with Time Delay and Unknown Parameters," Complexity, Hindawi, vol. 2020, pages 1-14, May.
    2. Yuan, Manman & Wang, Weiping & Luo, Xiong & Liu, Linlin & Zhao, Wenbing, 2018. "Finite-time anti-synchronization of memristive stochastic BAM neural networks with probabilistic time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 244-260.
    3. Mathiyalagan, K. & Park, Ju H. & Sakthivel, R., 2015. "Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 967-979.
    4. E. L. Pankratov & B. Spagnolo, 2005. "Optimization of impurity profile for p-n-junction in heterostructures," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 46(1), pages 15-19, July.
    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. Ganesan, Bhuvaneshwari & Annamalai, Manivannan, 2023. "Anti-synchronization analysis of chaotic neural networks using delay product type looped-Lyapunov functional," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Kumar, Ankit & Das, Subir & Singh, Sunny & Rajeev,, 2023. "Quasi-projective synchronization of inertial complex-valued recurrent neural networks with mixed time-varying delay and mismatched parameters," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    3. Yuan, Manman & Luo, Xiong & Mao, Xue & Han, Zhen & Sun, Lei & Zhu, Peican, 2022. "Event-triggered hybrid impulsive control on lag synchronization of delayed memristor-based bidirectional associative memory neural networks for image hiding," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    4. Gu, Yang & Shao, Yiyu & Li, Liwei & Shen, Mouquan, 2024. "Event-triggered fault tolerant control for Markov jump systems via a proportional–integral intermediate estimator," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    5. Jiejie Fan & Xiaojuan Ban & Manman Yuan & Wenxing Zhang, 2024. "Pinning Event-Triggered Scheme for Synchronization of Delayed Uncertain Memristive Neural Networks," Mathematics, MDPI, vol. 12(6), pages 1-28, March.
    6. Karnan, A. & Nagamani, G., 2023. "Event-triggered extended dissipative synchronization for delayed neural networks with random uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

    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. Abdurahman, Abdujelil & Abudusaimaiti, Mairemunisa & Jiang, Haijun, 2023. "Fixed/predefined-time lag synchronization of complex-valued BAM neural networks with stochastic perturbations," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    2. Chen, Dazhao & Zhang, Zhengqiu, 2022. "Finite-time synchronization for delayed BAM neural networks by the approach of the same structural functions," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    3. Tu, Zhengwen & Ding, Nan & Li, Liangliang & Feng, Yuming & Zou, Limin & Zhang, Wei, 2017. "Adaptive synchronization of memristive neural networks with time-varying delays and reaction–diffusion term," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 118-128.
    4. 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).
    5. Kim, Tae-Hyeon & Kim, Sungjoon & Hong, Kyungho & Park, Jinwoo & Hwang, Yeongjin & Park, Byung-Gook & Kim, Hyungjin, 2021. "Multilevel switching memristor by compliance current adjustment for off-chip training of neuromorphic system," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    6. Choi, Woo Sik & Jang, Jun Tae & Kim, Donguk & Yang, Tae Jun & Kim, Changwook & Kim, Hyungjin & Kim, Dae Hwan, 2022. "Influence of Al2O3 layer on InGaZnO memristor crossbar array for neuromorphic applications," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    7. Qin, Xiaoli & Wang, Cong & Li, Lixiang & Peng, Haipeng & Yang, Yixian & Ye, Lu, 2018. "Finite-time modified projective synchronization of memristor-based neural network with multi-links and leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 302-315.
    8. Chen, Mengshen & Yang, Xiaofei & Shen, Hao & Yao, Fengqi, 2016. "Finite-time asynchronous H∞ control for Markov jump repeated scalar non-linear systems with input constraints," Applied Mathematics and Computation, Elsevier, vol. 275(C), pages 172-180.
    9. Alsuwian, Turki & Kousar, Farhana & Rasheed, Umbreen & Imran, Muhammad & Hussain, Fayyaz & Arif Khalil, R.M. & Algadi, Hassan & Batool, Najaf & Khera, Ejaz Ahmad & Kiran, Saira & Ashiq, Muhammad Naeem, 2021. "First principles investigation of physically conductive bridge filament formation of aluminum doped perovskite materials for neuromorphic memristive applications," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    10. Filatov, D.O. & Koryazhkina, M.N. & Novikov, A.S. & Shishmakova, V.A. & Shenina, M.E. & Antonov, I.N. & Gorshkov, O.N. & Agudov, N.V. & Carollo, A. & Valenti, D. & Spagnolo, B., 2022. "Effect of internal noise on the relaxation time of an yttria stabilized zirconia-based memristor," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    11. Ratnavelu, K. & Manikandan, M. & Balasubramaniam, P., 2015. "Synchronization of fuzzy bidirectional associative memory neural networks with various time delays," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 582-605.
    12. Guarcello, C., 2021. "Lévy noise effects on Josephson junctions," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    13. Revin, L.S. & Pankratov, A.L., 2021. "Detection of bias inhomogeneity in Josephson junctions by switching current distributions," Chaos, Solitons & Fractals, Elsevier, vol. 149(C).
    14. Zeng, Deqiang & Zhang, Ruimei & Liu, Yajuan & Zhong, Shouming, 2017. "Sampled-data synchronization of chaotic Lur’e systems via input-delay-dependent-free-matrix zero equality approach," Applied Mathematics and Computation, Elsevier, vol. 315(C), pages 34-46.
    15. Yuan, Manman & Luo, Xiong & Mao, Xue & Han, Zhen & Sun, Lei & Zhu, Peican, 2022. "Event-triggered hybrid impulsive control on lag synchronization of delayed memristor-based bidirectional associative memory neural networks for image hiding," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    16. Wang, Fen & Chen, Yuanlong, 2021. "Mean square exponential stability for stochastic memristor-based neural networks with leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    17. Li, Ruoxia & Gao, Xingbao & Cao, Jinde, 2019. "Quasi-state estimation and quasi-synchronization control of quaternion-valued fractional-order fuzzy memristive neural networks: Vector ordering approach," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    18. Gao, Bo & Deng, Zheng-hong & Zhao, Da-wei & Song, Qun, 2017. "State analysis of Boolean control networks with impulsive and uncertain disturbances," Applied Mathematics and Computation, Elsevier, vol. 301(C), pages 187-192.
    19. Li, Hong-Li & Hu, Cheng & Jiang, Yao-Lin & Wang, Zuolei & Teng, Zhidong, 2016. "Pinning adaptive and impulsive synchronization of fractional-order complex dynamical networks," Chaos, Solitons & Fractals, Elsevier, vol. 92(C), pages 142-149.
    20. Li, Ruoxia & Cao, Jinde & Alsaedi, Ahmad & Alsaadi, Fuad, 2017. "Exponential and fixed-time synchronization of Cohen–Grossberg neural networks with time-varying delays and reaction-diffusion terms," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 37-51.

    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:150:y:2021:i:c:s0960077921004641. 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.