IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v175y2023ip1s0960077923008457.html
   My bibliography  Save this article

Synchronization and control for directly coupled reaction–diffusion neural networks with multiweights and hybrid coupling

Author

Listed:
  • Lin, Shanrong
  • Liu, Xiwei

Abstract

This paper mainly deals with the synchronization and pinning control for multiweighted, directly, and hybridly coupled reaction–diffusion neural networks (MDHCRDNNs). Different communication channels are expressed by multiple coupling matrices, while hybrid coupling means that state information combined with spatial diffusion information are employed jointly to attain synchronization. In comparison to previously published literature on multiweighted networks, outer matrices (OMs) in our paper can be directly coupled, with negative elements, and not even connected. One novel synchronization strategy is proposed to address directed networks with multiweights by integrating state matrices and spatial matrices into new union matrices. Then, for MDHCRDNNs, we obtain if the weighted groups of added OMs for each dimension are strongly connected, then synchronization and pinning synchronization criteria are derived. Furthermore, synchronization for adaptive coupling strength is solved as well. Finally, the effectiveness of these obtained results is verified through simulation examples.

Suggested Citation

  • Lin, Shanrong & Liu, Xiwei, 2023. "Synchronization and control for directly coupled reaction–diffusion neural networks with multiweights and hybrid coupling," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    • Handle: RePEc:eee:chsofr:v:175:y:2023:i:p1:s0960077923008457
    DOI: 10.1016/j.chaos.2023.113944
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923008457
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2023.113944?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. Zhu, Liang & Wang, Youguo, 2018. "Rumor diffusion model with spatio-temporal diffusion and uncertainty of behavior decision in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 29-39.
    2. Feng, Jiqiang & Li, Yongcai & Zhang, Yingfang & Xu, Chen, 2023. "Stabilization of multi-link delayed neutral-type complex networks with jump diffusion via aperiodically intermittent control," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    3. Kiruthika, R. & Krishnasamy, R. & Lakshmanan, S. & Prakash, M. & Manivannan, A., 2023. "Non-fragile sampled-data control for synchronization of chaotic fractional-order delayed neural networks via LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    4. Lu, Jun Guo, 2008. "Global exponential stability and periodicity of reaction–diffusion delayed recurrent neural networks with Dirichlet boundary conditions," Chaos, Solitons & Fractals, Elsevier, vol. 35(1), pages 116-125.
    5. Assali, El Abed, 2021. "Predefined-time synchronization of chaotic systems with different dimensions and applications," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    6. Mathiyalagan, K. & Renugadevi, T. & Nidhi, A. Shree & Ma, Yong-Ki & Cao, Jinde, 2022. "Boundary state feedback control for semilinear fractional-order reaction diffusion systems," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    7. Xiu, Chunbo & Zhou, Ruxia & Liu, Yuxia, 2020. "New chaotic memristive cellular neural network and its application in secure communication system," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    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. 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).
    2. Jibin Yang & Xiaohui Xu & Quan Xu & Haolin Yang & Mengge Yu, 2024. "Stability and Synchronization of Delayed Quaternion-Valued Neural Networks under Multi-Disturbances," Mathematics, MDPI, vol. 12(6), pages 1-26, March.
    3. Li, Xinna & Wu, Huaiqin & Cao, Jinde, 2023. "Prescribed-time synchronization in networks of piecewise smooth systems via a nonlinear dynamic event-triggered control strategy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 647-668.
    4. M. Syed Ali & Gani Stamov & Ivanka Stamova & Tarek F. Ibrahim & Arafa A. Dawood & Fathea M. Osman Birkea, 2023. "Global Asymptotic Stability and Synchronization of Fractional-Order Reaction–Diffusion Fuzzy BAM Neural Networks with Distributed Delays via Hybrid Feedback Controllers," Mathematics, MDPI, vol. 11(20), pages 1-24, October.
    5. Hayrengul Sadik & Abdujelil Abdurahman & Rukeya Tohti, 2023. "Fixed-Time Synchronization of Reaction-Diffusion Fuzzy Neural Networks with Stochastic Perturbations," Mathematics, MDPI, vol. 11(6), pages 1-15, March.
    6. Gani Stamov & Ivanka Stamova & George Venkov & Trayan Stamov & Cvetelina Spirova, 2020. "Global Stability of Integral Manifolds for Reaction–Diffusion Delayed Neural Networks of Cohen–Grossberg-Type under Variable Impulsive Perturbations," Mathematics, MDPI, vol. 8(7), pages 1-18, July.
    7. Hongbo Cao & Faqiang Wang, 2023. "An Overview of Complex Instability Behaviors Induced by Nonlinearity of Power Electronic Systems with Memristive Load," Energies, MDPI, vol. 16(6), pages 1-25, March.
    8. Mei, Yu & Wang, Guanqi & Shen, Hao, 2023. "Adaptive Event-Triggered L2−L∞ Control of Semi-Markov Jump Distributed Parameter Systems," Applied Mathematics and Computation, Elsevier, vol. 439(C).
    9. Andrei D. Polyanin & Vsevolod G. Sorokin, 2023. "Reductions and Exact Solutions of Nonlinear Wave-Type PDEs with Proportional and More Complex Delays," Mathematics, MDPI, vol. 11(3), pages 1-25, January.
    10. Zhang, Mengjiao & Zang, Hongyan & Bai, Luyuan, 2022. "A new predefined-time sliding mode control scheme for synchronizing chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    11. Ke, Yue & Zhu, Linhe & Wu, Peng & Shi, Lei, 2022. "Dynamics of a reaction-diffusion rumor propagation model with non-smooth control," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    12. Vsevolod G. Sorokin & Andrei V. Vyazmin, 2022. "Nonlinear Reaction–Diffusion Equations with Delay: Partial Survey, Exact Solutions, Test Problems, and Numerical Integration," Mathematics, MDPI, vol. 10(11), pages 1-39, May.
    13. Zhang, Hai & Cheng, Yuhong & Zhang, Weiwei & Zhang, Hongmei, 2023. "Time-dependent and Caputo derivative order-dependent quasi-uniform synchronization on fuzzy neural networks with proportional and distributed delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 846-857.
    14. Zhang, Yuhuai & Zhu, Jianjun, 2019. "Dynamic behavior of an I2S2R rumor propagation model on weighted contract networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    15. Sahoo, Shilalipi & Nathasarma, Rahash & Roy, Binoy Krishna, 2024. "Time-synchronized predefined-time synchronization between two non-identical chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    16. Lu, Peng & Yao, Qi & Lu, Pengfei, 2019. "Two-stage predictions of evolutionary dynamics during the rumor dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 349-369.
    17. Zhu, Linhe & Chen, Siyi & Shen, Shuling, 2024. "Pattern dynamics analysis of a reaction–diffusion network propagation model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 220(C), pages 425-444.
    18. Xue, Haibo & Liu, Xinghua, 2023. "A novel fast terminal sliding mode with predefined-time synchronization," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    19. 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).
    20. Xue Yang & Zhiliang Zhu & Hai Yu & Yuli Zhao & Li Guo, 2019. "Evolutionary Game Dynamics of the Competitive Information Propagation on Social Networks," Complexity, Hindawi, vol. 2019, pages 1-11, December.

    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:175:y:2023:i:p1:s0960077923008457. 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.