Skip to main content
SpringerLink
Log in

Event-Triggered quasi-synchronization of neural networks with hidden Markov model-based asynchronous target

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

This article is concerned with the event-triggered quasi-synchronization for discrete Markov jump neural networks (MJNNs). Considering that the slave system cannot capture synchronously master system modes in real-world applications, a hidden Markov model is introduced to describe the resultant mode mismatches. To pursue a desired balance between the synchronization performance and the event-triggered transmission, a more general event-triggered protocol is constructed by developing the threshold parameter as a diagonal matrix. Subsequently, the sufficient condition for event-triggered quasi-synchronization of MJNNs is proposed with the assistance of Lyapunov techniques. Moreover, resorting to an iterative algorithm and the linear matrix inequality, the tighter error bound is obtained. Finally, a numerical example demonstrates effectiveness of the control scheme via a comparison of conservatism between the proposed approach and the existing one.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data availability

No data were used for the current study in this paper.

References

  1. Ahn, C.K.: Switched exponential state estimation of neural networks based on passivity theory. Nonlinear Dyn. 67, 573–586 (2012)

    MathSciNet  MATH  Google Scholar 

  2. Shen, H., Huang, Z., Yang, X., Wang, Z.: Quantized energy-to-peak state estimation for persistent dwell-time switched neural networks with packet dropouts. Nonlinear Dyn. 93(4), 2249–2262 (2018)

    MATH  Google Scholar 

  3. Lin, G., Li, H., Ahn, C.K., Yao, D.: Event-based finite-time neural control for human-in-the-loop UAV attitude systems. IEEE Trans. Neural Netw. Learn. Syst. (2022). https://doi.org/10.1109/TNNLS.2022.3166531

    Article  Google Scholar 

  4. Ni, Y., Wang, Z., Huang, X., Ma, Q., Shen, H.: Intermittent sampled-data control for local stabilization of neural networks subject to actuator saturation: a work-interval-dependent functional approach. IEEE Trans. Neural Netw. Learn. Syst. (2022). https://doi.org/10.1109/TNNLS.2022.3180076

    Article  Google Scholar 

  5. Li, C., Wu, S., Feng, G.G., Liao, X.: Stabilizing effects of impulses in discrete-time delayed neural networks. IEEE Trans. Neural Netw. 22(2), 323–329 (2011)

    Google Scholar 

  6. Li, C., Feng, G., Huang, T.: On hybrid impulsive and switching neural networks. IEEE Trans. Syst. Man Cyber. Part B (Cybernetics) 38(6), 1549–1560 (2008)

    Google Scholar 

  7. Stamov, G., Gospodinova, E., Stamova, I.: Practical exponential stability with respect to h-manifolds of discontinuous delayed Cohen–Grossberg neural networks with variable impulsive perturbations. Math. Model. Control 1, 26–34 (2021)

    Google Scholar 

  8. Tino, P., Cernansky, M., Benuskova, L.: Markovian architectural bias of recurrent neural networks. IEEE Trans. Neural Netw. 15(1), 6–15 (2004)

    Google Scholar 

  9. Shen, H., Zhu, Y., Zhang, L., Park, J.H.: Extended dissipative state estimation for Markov jump neural networks with unreliable links. IEEE Trans. Neural Netw. Learn. Syst. 28(2), 346–358 (2016)

    MathSciNet  Google Scholar 

  10. Tao, J., Wu, Z.G., Su, H., Wu, Y., Zhang, D.: Asynchronous and resilient filtering for Markovian jump neural networks subject to extended dissipativity. IEEE Trans. Cyber. 49(7), 2504–2513 (2018)

    Google Scholar 

  11. Wang, J., Xing, M., Cao, J., Park, J.H., Shen, H.: \({H}_\infty \) bipartite synchronization of double-layer markov switched cooperation-competition neural networks: a distributed dynamic event-triggered mechanism. IEEE Trans. Neural Netw. Learn. Syst. (2021)

  12. Yao, L., Wang, Z., Huang, X., Li, Y., Ma, Q., Shen, H.: Stochastic sampled-data exponential synchronization of Markovian jump neural networks with time-varying delays. IEEE Trans. Neural Netw. Learn. Syst. 34(2), 909–920 (2023)

    MathSciNet  Google Scholar 

  13. Mirollo, R.E., Strogatz, S.H.: Synchronization of pulse-coupled biological oscillators. SIAM J. Appl. Math. 50(6), 1645–1662 (1990)

    MathSciNet  MATH  Google Scholar 

  14. Xu, Z., Li, C., Han, Y.: Impulsive consensus of nonlinear multi-agent systems via edge event-triggered control. IEEE Trans. Neural Netw. Learn. Syst. 31(6), 1995–2004 (2019)

    MathSciNet  Google Scholar 

  15. Wang, X., Wang, H., Li, C., Huang, T., Kurths, J.: Consensus seeking in multiagent systems with Markovian switching topology under aperiodic sampled data. IEEE Trans. Syst. Man Cyber Syst 50(12), 5189–5200 (2018)

    Google Scholar 

  16. Chen, C., Xie, K., Lewis, F.L., Xie, S., Davoudi, A.: Fully distributed resilience for adaptive exponential synchronization of heterogeneous multiagent systems against actuator faults. IEEE Trans. Autom. Control 64(8), 3347–3354 (2018)

    MathSciNet  MATH  Google Scholar 

  17. Wang, X., Wang, H., Huang, T., Kurths, J.: Neural-network-based adaptive tracking control for nonlinear multiagent systems: the observer case. IEEE Trans. Cyber. 53(1), 138–150 (2023)

    Google Scholar 

  18. Ahmed, I., Rehan, M., Iqbal, N., Ahn, C.K.: A novel event-triggered consensus approach for generic linear multi-agents under heterogeneous sector-restricted input nonlinearities. IEEE Trans. Netw. Sci. Eng. (2023). https://doi.org/10.1109/TNSE.2022.3232779

    Article  MathSciNet  Google Scholar 

  19. Zhou, X., Cheng, J., Cao, J., Ragulskis, M.: Asynchronous dissipative filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts. Neural Netw. 130, 229–237 (2020)

    MATH  Google Scholar 

  20. Zhang, L., Zhong, J., Lu, J.: Intermittent control for finite-time synchronization of fractional-order complex networks. Neural Netw. 144, 11–20 (2021)

    Google Scholar 

  21. Hansel, D., Sompolinsky, H.: Synchronization and computation in a chaotic neural network. Phys. Rev. Lett. 68(5), 718 (1992)

    Google Scholar 

  22. Cao, J., Wan, Y.: Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays. Neural Netw. 53, 165–172 (2014)

    MATH  Google Scholar 

  23. Zhang, F., Zeng, Z.: Asymptotic stability and synchronization of fractional-order neural networks with unbounded time-varying delays. IEEE Trans. Syst. Man Cyber. Syst. 51(9), 5547–5556 (2019)

    Google Scholar 

  24. Li, H., Li, C., Ouyang, D., Nguang, S.K.: Impulsive synchronization of unbounded delayed inertial neural networks with actuator saturation and sampled-data control and its application to image encryption. IEEE Trans. Neural Netw. Learn. Syst. 32(4), 1460–1473 (2020)

    MathSciNet  Google Scholar 

  25. Wang, Y., Lu, J., Li, X., Liang, J.: Synchronization of coupled neural networks under mixed impulsive effects: a novel delay inequality approach. Neural Netw. 127, 38–46 (2020)

    MATH  Google Scholar 

  26. Shen, H., Li, F., Wu, Z.G., Park, J.H.: Finite-time \(l_2-l_\infty \) tracking control for Markov jump repeated scalar nonlinear systems with partly usable model information. Inf. Sci. 332, 153–166 (2016)

    MATH  Google Scholar 

  27. He, W., Qian, F., Lam, J., Chen, G., Han, Q.L., Kurths, J.: Quasi-synchronization of heterogeneous dynamic networks via distributed impulsive control: error estimation, optimization and design. Automatica 62, 249–262 (2015)

    MathSciNet  MATH  Google Scholar 

  28. Rao, H., Xu, Y., Peng, H., Lu, R., Su, C.Y.: Quasi-synchronization of time delay Markovian jump neural networks with impulsive-driven transmission and fading channels. IEEE Trans. Cyber. 50(9), 4121–4131 (2019)

    Google Scholar 

  29. Yue, D., Tian, E., Han, Q.L.: A delay system method for designing event-triggered controllers of networked control systems. IEEE Trans. Autom. Control 58(2), 475–481 (2012)

    MathSciNet  MATH  Google Scholar 

  30. Zhang, J., Raissi, T.: Indefinite Lyapunov-Razumikhin functions-based stability and event-triggered control of switched nonlinear time-delay systems. IEEE Trans. Circ. Syst. II Express Briefs 68(10), 3286–3290 (2021)

    Google Scholar 

  31. Tao, J., Xu, M., Chen, D., Xiao, Z., Rao, H., Xu, Y.: Event-triggered resilient filtering with the interval type uncertainty for Markov jump systems. IEEE Trans. Cyber. (2022). https://doi.org/10.1109/TCYB.2022.3227446

    Article  Google Scholar 

  32. Liu, W., Yang, X., Rakkiyappan, R., Li, X.: Dynamic analysis of delayed neural networks: event-triggered impulsive Halanay inequality approach. Neurocomputing 498, 98–107 (2022)

    Google Scholar 

  33. Tao, J., Xiao, Z., Chen, J., Lin, M., Lu, R., Shi, P., Wang, X.: Event-triggered control for Markov jump systems subject to mismatched modes and strict dissipativity. IEEE Trans. Cyber. 53(3), 1537–1546 (2023)

    Google Scholar 

  34. Gu, Z., Ahn, C.K., Yan, S., Xie, X., Yue, D.: Event-triggered filter design based on average measurement output for networked unmanned surface vehicles. IEEE Trans. Circ. Syst. II Express Briefs 69(9), 3804–3808 (2022)

    Google Scholar 

  35. Wang, J., Chen, M., Shen, H.: Event-triggered dissipative filtering for networked semi-Markov jump systems and its applications in a mass-spring system model. Nonlinear Dyn. 87(4), 2741–2753 (2017)

    MathSciNet  MATH  Google Scholar 

  36. Wang, J., Zhang, X.M., Lin, Y., Ge, X., Han, Q.L.: Event-triggered dissipative control for networked stochastic systems under non-uniform sampling. Inf. Sci. 447, 216–228 (2018)

    MATH  Google Scholar 

  37. Dai, M., Xia, J., Xia, H., Shen, H.: Event-triggered passive synchronization for Markov jump neural networks subject to randomly occurring gain variations. Neurocomputing 331, 403–411 (2019)

    Google Scholar 

  38. Zhang, J., Zheng, G., Feng, Y., Chen, Y.: Event-triggered state-feedback and dynamic output-feedback control of positive Markovian jump systems with intermittent faults. IEEE Trans. Autom. Control 68(2), 1039–1046 (2023)

    Google Scholar 

  39. Yan, L., Chen, W., Fang, X., Dai, H.: Event-triggered synchronization for second-order nodes in complex dynamical network with time-varying coupling matrices. Nonlinear Dyn. 98(3), 2227–2245 (2019)

    MATH  Google Scholar 

  40. Wen, S., Zeng, Z., Chen, M.Z., Huang, T.: Synchronization of switched neural networks with communication delays via the event-triggered control. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2334–2343 (2016)

    MathSciNet  Google Scholar 

  41. Yang, J., Lu, J., Li, L., Liu, Y., Wang, Z., Alsaadi, F.E.: Event-triggered control for the synchronization of Boolean control networks. Nonlinear Dyn. 96(2), 1335–1344 (2019)

    MATH  Google Scholar 

  42. Xu, Y., Huang, Z., Rao, H., Lu, R., Huang, T.: Quasi-synchronization for periodic neural networks with asynchronous target and constrained information. IEEE Trans. Syst. Man Cyber Syst (2019)

  43. Wang, S., Cao, Y., Guo, Z., Yan, Z., Wen, S., Huang, T.: Periodic event-triggered synchronization of multiple memristive neural networks with switching topologies and parameter mismatch. IEEE Trans. Cyber (2020)

  44. Dong, S., Liu, M.: Adaptive fuzzy asynchronous control for nonhomogeneous Markov jump power systems under hybrid attacks. IEEE Trans. Fuzzy Syst. 31(3), 1009–1019 (2023)

    Google Scholar 

  45. Li, F., Xu, S., Shen, H., Ma, Q.: Passivity-based control for hidden Markov jump systems with singular perturbations and partially unknown probabilities. IEEE Trans. Autom. Control 65(8), 3701–3706 (2019)

    MathSciNet  MATH  Google Scholar 

  46. Dong, S., Xie, K., Chen, G., Liu, M., Wu, Z.G.: Extended dissipative sliding-mode control for discrete-time piecewise nonhomogeneous Markov jump nonlinear systems. IEEE Trans. Cyber. 52(9), 9219–9229 (2021)

    Google Scholar 

  47. Wu, Z., Chen, J., Zhang, X., Xiao, Z., Tao, J., Wang, X.: Dynamic event-triggered synchronization of complex networks with switching topologies: asynchronous observer-based case. Appl. Math. Comput. 435, 127413 (2022)

    MathSciNet  MATH  Google Scholar 

  48. Cheng, P., He, S., Luan, X., Liu, F.: Finite-region asynchronous \({H}_\infty \) control for 2D Markov jump systems. Automatica 129, 109590 (2021)

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the National Natural Science Foundation of China, and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control for supporting the authors’ study.

Funding

This work was supported by the National Natural Science Foundation of China (62276069,62121004), the Science and Technology Program of Guangzhou (202201010337), and the Natural Science Foundation of Guangdong Province (2022A1515010271).

Author information

Authors and Affiliations

  1. Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou, 510006, China

    Zhenyu Wu, Zehui Xiao & Jie Tao

  2. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Zhenyu Wu, Zehui Xiao, Xuexi Zhang & Jie Tao

Authors
  1. Zhenyu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zehui Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuexi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study. ZW and ZX contributed to conceptualization, visualization, validation, and methodology. Funding acquisition and supervision were performed by JT and XZ. The original draft was written by ZW. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xuexi Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Wu, Z., Xiao, Z., Zhang, X. et al. Event-Triggered quasi-synchronization of neural networks with hidden Markov model-based asynchronous target. Nonlinear Dyn 111, 16145–16157 (2023). https://doi.org/10.1007/s11071-023-08679-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-023-08679-1

Keywords

Search

Navigation