Skip to main content
Log in

Quasisynchronization of reaction-diffusion neural networks with time-varying delays by static/dynamic event-triggered control and its application to secure communication

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper studies the quasisynchronization problems of reaction-diffusion neural networks (RDNNs) with time-varying delays via event-triggered control. Firstly, a static event-triggered mechanism and a dynamic event-triggered mechanism are designed to significantly reduce computation costs and save communication resources, respectively. These two different event-triggered control strategies are also able to meet the requirements of various situations. Based on the static event-triggered mechanism, the dynamic event-triggered mechanism is designed to further reduce the sampling frequency by introducing an internal dynamic variable, and several quasisynchronization criteria are derived. However, the quasisynchronization error bounds are related to triggering parameters and can be flexible adjusted, which reduces the conservatism of the existing quasisynchronization results and extends the application of proposed control strategies. Meanwhile, there exists positive lower bounds for the inter event time which can exclude the Zeno behavior. Finally, numerical simulations are given to demonstrate the superiority of the obtained theoretical results, and one example is given to show the chaotic quasisynchronization of the proposed RDNNs in the application of secure communication.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Algorithm 1
Fig. 9

Similar content being viewed by others

Data availibility statement

The data used during the current study are available from the corresponding author upon reasonable request.

References

  1. Hopfield JJ, Tank DW (1986) Computing with neural circuits: a model. Science 233(4764):625–633

    Article  Google Scholar 

  2. Marcus C, Westervelt R (1989) Stability of analog neural networks with delay. Phys Rev A 39(1):347

    Article  MathSciNet  Google Scholar 

  3. Zhang N, Wang X, Li W (2022) Stability for multi-linked stochastic delayed complex networks with stochastic hybrid impulses by Dupire Itô’s formula. Nonlinear Anal Hybrid Syst 45:101200

    Article  Google Scholar 

  4. Lin H, Wang C, Tan Y (2020) Hidden extreme multistability with hyperchaos and transient chaos in a hopfield neural network affected by electromagnetic radiation. Nonlinear Dyn 99(3):2369–2386

    Article  Google Scholar 

  5. Manivannan R, Cao Y, Chong KT (2022) Unified dissipativity state estimation for delayed generalized impulsive neural networks with leakage delay effects. Knowl Based Syst 254:109630

    Article  Google Scholar 

  6. Lee TH, Park M-J, Park JH, Kwon O-M, Lee S-M (2014) Extended dissipative analysis for neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst 25(10):1936–1941

    Article  Google Scholar 

  7. Cao J, Wang J (2005) Global exponential stability and periodicity of recurrent neural networks with time delays. IEEE Trans Circuits Syst I Regular Pap 52(5):920–931

    Article  MathSciNet  Google Scholar 

  8. Wang L, He H, Zeng Z (2020) Global synchronization of fuzzy memristive neural networks with discrete and distributed delays. IEEE Trans Fuzzy Syst 28(9):2022–2034

    Article  Google Scholar 

  9. Ping J, Zhu S, Liu X (2022) Finite/fixed-time synchronization of memristive neural networks via event-triggered control. Knowl Based Syst, 110013

  10. Tong D, Liu X, Chen Q, Zhou W, Liao K (2022) Observer-based adaptive finite-time prescribed performance NN control for nonstrict-feedback nonlinear systems. Neural Comput Appl 34(15):12789–12805

    Article  Google Scholar 

  11. Zhang S, Yu Y, Wang H (2015) Mittag-leffler stability of fractional-order hopfield neural networks. Nonlinear Anal Hybrid Syst 16:104–121

    Article  MathSciNet  Google Scholar 

  12. Ozcan N (2019) Stability analysis of cohen-grossberg neural networks of neutral-type: multiple delays case. Neural Netw 113:20–27

    Article  Google Scholar 

  13. Mani P, Rajan R, Shanmugam L, Joo YH (2019) Adaptive control for fractional order induced chaotic fuzzy cellular neural networks and its application to image encryption. Inf Sci 491:74–89

    Article  MathSciNet  Google Scholar 

  14. Cao Y, Cao Y, Guo Z, Huang T, Wen S (2020) Global exponential synchronization of delayed memristive neural networks with reaction-diffusion terms. Neural Netw 123:70–81

    Article  Google Scholar 

  15. Xu Y, Sun F, Li W (2021) Exponential synchronization of fractional-order multilayer coupled neural networks with reaction-diffusion terms via intermittent control. Neural Comput Appl 33(23):16019–16032

    Article  Google Scholar 

  16. Hu X, Wang L, Zhang C-K, Wan X, He Y (2023) Fixed-time stabilization of discontinuous spatiotemporal neural networks with time-varying coefficients via aperiodically switching control. Sci China Inf Sci 66(5):1–14

    Article  MathSciNet  Google Scholar 

  17. Tyagi S, Abbas S, Kirane M (2018) Global asymptotic and exponential synchronization of ring neural network with reaction-diffusion term and unbounded delay. Neural Comput Appl 30:487–501

    Article  Google Scholar 

  18. Li X-Y, Fan Q-L, Liu X-Z, Wu K-N (2022) Boundary intermittent stabilization for delay reaction-diffusion cellular neural networks. Neural Comput Appl 34(21):18561–18577

    Article  Google Scholar 

  19. Rakkiyappan R, Dharani S (2017) Sampled-data synchronization of randomly coupled reaction-diffusion neural networks with markovian jumping and mixed delays using multiple integral approach. Neural Comput Appl 28:449–462

    Article  Google Scholar 

  20. Cao Y, Jiang W, Wang J (2021) Anti-synchronization of delayed memristive neural networks with leakage term and reaction-diffusion terms. Knowl Based Syst 233:107539

    Article  Google Scholar 

  21. Wang J-L, Wu H-N, Huang T (2015) Passivity-based synchronization of a class of complex dynamical networks with time-varying delay. Automatica 56:105–112

    Article  MathSciNet  Google Scholar 

  22. Arenas A, Díaz-Guilera A, Kurths J, Moreno Y, Zhou C (2008) Synchronization in complex networks. Phys Rep 469(3):93–153

    Article  MathSciNet  Google Scholar 

  23. Assaneo MF, Ripollés P, Orpella J, Lin WM, Diego-Balaguer R, Poeppel D (2019) Spontaneous synchronization to speech reveals neural mechanisms facilitating language learning. Nat Neurosci 22(4):627–632

    Article  Google Scholar 

  24. Kose MA, Prasad ES, Terrones ME (2003) How does globalization affect the synchronization of business cycles? Am Econ Rev 93(2):57–62

    Article  Google Scholar 

  25. Tong D, Ma B, Chen Q, Wei Y, Shi P (2023) Finite-time synchronization and energy consumption prediction for multilayer fractional-order networks. IEEE Trans Circuits Syst II Exp Briefs 70(6):2176–2180

    Google Scholar 

  26. Zhang R, Zeng D, Park JH, Lam H-K, Xie X (2021) Fuzzy sampled-data control for synchronization of t-s fuzzy reaction-diffusion neural networks with additive time-varying delays. IEEE Trans Cybernet 51(5):2384–2397

    Article  Google Scholar 

  27. Cao Z, Li C, He Z, Zhang X, You L (2022) Synchronization of coupled stochastic reaction-diffusion neural networks with multiple weights and delays via pinning impulsive control. IEEE Trans Netw Sci Eng 9(2):820–833

    Article  MathSciNet  Google Scholar 

  28. Wang J-L, Wu H-N, Huang T, Ren S-Y, Wu J (2016) Pinning control for synchronization of coupled reaction-diffusion neural networks with directed topologies. IEEE Trans Syst Man Cybernet Syst 46(8):1109–1120

    Article  Google Scholar 

  29. Zhang H, Ding Z, Zeng Z (2020) Adaptive tracking synchronization for coupled reaction-diffusion neural networks with parameter mismatches. Neural Netw 124:146–157

    Article  Google Scholar 

  30. Song X, Man J, Song S, Ahn CK (2021) Finite/fixed-time anti-synchronization of inconsistent markovian quaternion-valued memristive neural networks with reaction-diffusion terms. IEEE Trans Circuits Syst I Regular Pap 68(1):363–375

    Article  MathSciNet  Google Scholar 

  31. Shanmugam L, Mani P, Rajan R, Joo YH (2020) Adaptive synchronization of reaction-diffusion neural networks and its application to secure communication. IEEE Trans Cybernet 50(3):911–922

    Article  Google Scholar 

  32. Chen W, Yu Y, Hai X, Ren G (2022) Adaptive quasi-synchronization control of heterogeneous fractional-order coupled neural networks with reaction-diffusion. Appl Math Comput 427:127145

    MathSciNet  Google Scholar 

  33. Zhang R, Wang H, Park JH, Lam H-K, He P (2022) Quasisynchronization of reaction-diffusion neural networks under deception attacks. IEEE Trans Syst Man Cybernet Syst 52(12):7833–7844

    Article  Google Scholar 

  34. Song X, Li X, Song S, Zhang Y, Ning Z (2021) Quasi-synchronization of coupled neural networks with reaction-diffusion terms driven by fractional brownian motion. J Franklin Inst 358(4):2482–2499

    Article  MathSciNet  Google Scholar 

  35. Lu B, Jiang H, Hu C, Abdurahman A (2020) Spacial sampled-data control for h\(\infty\) output synchronization of directed coupled reaction-diffusion neural networks with mixed delays. Neural Netw 123:429–440

    Article  Google Scholar 

  36. Cao Y, Liu N, Zhang C, Zhang T, Luo Z-F (2022) Synchronization of multiple reaction-diffusion memristive neural networks with known or unknown parameters and switching topologies. Knowl Based Syst 254:109595

    Article  Google Scholar 

  37. Zeng D, Zhang R, Park JH, Pu Z, Liu Y (2020) Pinning synchronization of directed coupled reaction-diffusion neural networks with sampled-data communications. IEEE Trans Neural Netw Learn Syst 31(6):2092–2103

    Article  MathSciNet  Google Scholar 

  38. Liu Y, Lin Y (2022) Synchronization of quaternion-valued coupled systems with time-varying coupling via event-triggered impulsive control. Math Methods Appl Sci 45(1):324–340

    Article  MathSciNet  Google Scholar 

  39. Wang X, Feng G (2023) Dynamic Event-Triggered \(\cal{H}_{\infty }\) Filtering for NCSs under multiple cyber-attacks. IEEE Trans Syst Man Cybernet Syst. https://doi.org/10.1109/TSMC.2023.3256970

  40. Wang S, Cao Y, Guo Z, Yan Z, Wen S, Huang T (2021) Periodic event-triggered synchronization of multiple memristive neural networks with switching topologies and parameter mismatch. IEEE Trans Cybernet 51(1):427–437

    Article  Google Scholar 

  41. Vadivel R, Ali MS, Joo YH (2020) Drive-response synchronization of uncertain markov jump generalized neural networks with interval time varying delays via decentralized event-triggered communication scheme. J Franklin Inst 357(11):6824–6857

    Article  MathSciNet  Google Scholar 

  42. Jin Y, Qi W, Zong G (2021) Finite-time synchronization of delayed semi-markov neural networks with dynamic event-triggered scheme. Int J Control Autom Syst 19(6):2297–2308

    Article  Google Scholar 

  43. Cao Y, Wang S, Guo Z, Huang T, Wen S (2019) Synchronization of memristive neural networks with leakage delay and parameters mismatch via event-triggered control. Neural Netw 119:178–189

    Article  Google Scholar 

  44. Wen S, Zeng Z, Chen MZ, Huang T (2017) Synchronization of switched neural networks with communication delays via the event-triggered control. IEEE Trans Neural Netw Learn Syst 28(10):2334–2343

    Article  MathSciNet  Google Scholar 

  45. Zhou Y, Zhang H, Zeng Z (2022) Quasisynchronization of memristive neural networks with communication delays via event-triggered impulsive control. IEEE Trans Cybernet 52(8):7682–7693

    Article  Google Scholar 

  46. Cao Y, Wang S, Wen S (2019) Exponential synchronization of switched neural networks with mixed time-varying delays via static/dynamic event-triggering rules. IEEE Access 8:338–347

    Article  Google Scholar 

  47. Kazemy A, Lam J, Zhang X-M (2022) Event-triggered output feedback synchronization of master-slave neural networks under deception attacks. IEEE Trans Neural Netw Learn Syst 33(3):952–961

    Article  MathSciNet  Google Scholar 

  48. Wang X, Park JH, Liu Z, Yang H (2023) Dynamic event-triggered control for GSES of memristive neural networks under multiple cyber-attacks. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2022.3217461

  49. Cai J, Feng J, Wang J, Zhao Y (2020) Quasi-synchronization of neural networks with diffusion effects via intermittent control of regional division. Neurocomputing 409:146–156

    Article  Google Scholar 

  50. Chen W, Ren G, Yu Y, Yuan X (2023) Quasi-synchronization of heterogeneous stochastic coupled reaction-diffusion neural networks with mixed time-varying delays via boundary control. J Franklin Inst 360(13):10080–10099

    Article  MathSciNet  Google Scholar 

  51. Yang X, Cao J, Yang Z (2013) Synchronization of coupled reaction-diffusion neural networks with time-varying delays via pinning-impulsive controller. SIAM J Control Opt 51(5):3486–3510

    Article  MathSciNet  Google Scholar 

  52. Fan Y, Huang X, Li Y, Xia J, Chen G (2019) Aperiodically intermittent control for quasi-synchronization of delayed memristive neural networks: an interval matrix and matrix measure combined method. IEEE Trans Syst Man Cybernet Syst 49(11):2254–2265

    Article  Google Scholar 

  53. Xin Y, Li Y, Huang X, Cheng Z (2019) Quasi-synchronization of delayed chaotic memristive neural networks. IEEE Trans Cybernet 49(2):712–718

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chuanfu Zhang.

Ethics declarations

Conflict of interest

There is no conflict of interest in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, Y., Liu, N., Zhang, T. et al. Quasisynchronization of reaction-diffusion neural networks with time-varying delays by static/dynamic event-triggered control and its application to secure communication. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09778-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00521-024-09778-9

Keywords

Navigation