Skip to main content
SpringerLink
Log in

New Results on Finite-Time Synchronization of Delayed Fuzzy Neural Networks with Inertial Effects

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

This paper presents the theoretical results on the finite-time synchronization of two delayed inertial fuzzy neural networks. Different from the existing study about dynamics analysis for inertial neural networks, more adjustable parameters are introduced into the variable substitution, and a general Lyapunov function is constructed to realize synchronization in finite time. The analysis mainly employs the theory of finite-time stability theory, Lyapunov functional method and inequality techniques. Finally, numerical simulations are conducted to demonstrate the effectiveness of the theoretical results.

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

Similar content being viewed by others

References

  1. Yang, T., Yang, L., Wu, C., Chua, L.: Fuzzy cellular neural networks: theory. In: Proceedings of IEEE international workshop on cellular neural networks and applications, vol. 1, pp. 181–186. (1996)

  2. Marcus, C., Westervelt, R.: Stability of analog neural networks with time delay. Phys. Rev. A 39, 347–359 (1989)

    Article  MathSciNet  Google Scholar 

  3. Yang, T., Yang, L., Wu, C., Chua, L.: Fuzzy cellular neural networks: applications. In: Proceedings of IEEE international workshop on cellular neural networks and applications, vol. 1, pp. 225–230. (1996)

  4. Wang, S., Chung, K.F.L., Fu, D.: Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection. Neurocomputing 70, 1348–1359 (2007)

    Article  Google Scholar 

  5. Wang, S., Fu, D., Xu, M., Hu, D.: Advanced fuzzy cellular neural network: application to CT liver images. Artif. Intell. Med. 39, 65–77 (2007)

    Article  Google Scholar 

  6. Babcock, K., Westervelt, R.: Dynamics of simple electronic neural networks. Phys. D 28, 305–316 (1987)

    Article  MathSciNet  Google Scholar 

  7. Angelaki, D., Correia, M.: Models of membrane resonance in pigeon semicircular canal type II hair cells. Biol. Cybern. 65, 1–10 (1991)

    Article  Google Scholar 

  8. Koch, C.: Cable theory in neurons with active, linearized membranes. Biol. Cybern. 50, 15–33 (1984)

    Article  Google Scholar 

  9. Horikawa, Y., Kitajima, H.: Bifurcation and stabilization of oscillations in ring neural networks with inertia. Phys. D 238, 2409–2418 (2009)

    Article  Google Scholar 

  10. Wheeler, D.W., Schieve, W.C.: Stability and chaos in an inertial two-neuron system. Phys. D 105, 267–284 (1997)

    Article  Google Scholar 

  11. Xiao, Q., Huang, Z., Zeng, Z.: Passivity analysis for memristor-based inertial neural networks with discrete and distributed delays. IEEE Trans. Syst. Man Cybern. Syst. 49, 375–385 (2017)

    Article  Google Scholar 

  12. Prakash, M., Balasubramaniam, P., Lakshmanan, S.: Synchronization of Markovian jumping inertial neural networks and its applications in image encryption. Neural Netw. 83, 86–93 (2016)

    Article  Google Scholar 

  13. Xu, C., Zhang, Q., Wu, Y.: Existence and exponential stability of periodic solution to fuzzy cellular neural networks with distributed delays. Int. J. Fuzzy Syst. 18, 41–51 (2016)

    Article  MathSciNet  Google Scholar 

  14. Huang, C., Long, X., Cao, J.: Stability of antiperiodic recurrent neural networks with multiproportional delays. Math. Methods Appl. Sci. 43, 6093–6102 (2020)

    Article  MathSciNet  Google Scholar 

  15. Li, L., Wang, W., Huang, L., Wu, J.: Some weak flocking models and its application to target tracking. J. Math. Anal. Appl. 480, 123404 (2019)

    Article  MathSciNet  Google Scholar 

  16. Pecora, L., Carroll, T.: Synchronization in chaotic systems. Phys. Rev. Lett. 64, 821–824 (1990)

    Article  MathSciNet  Google Scholar 

  17. Feng, Y., Xiong, X., Tang, R., Yang, X.: Exponential synchronization of inertial neural networks with mixed delays via quantized pinning control. Neurocomputing 310, 165–171 (2018)

    Article  Google Scholar 

  18. Li, W., Gao, X., Li, R.: Stability and synchronization control of inertial neural networks with mixed delays. Appl. Math. Comput. 367, 124779 (2020)

    Article  MathSciNet  Google Scholar 

  19. Shi, J., Zeng, Z.: Anti-synchronization of delayed state-based switched inertial neural networks. IEEE Trans. Cybern. 51, 2540–2549 (2021)

    Article  Google Scholar 

  20. Li, K., Tong, S.: Fuzzy adaptive practical finite-time control for time delays nonlinear systems. Int. J. Fuzzy Syst. 21, 1013–1025 (2019)

    Article  MathSciNet  Google Scholar 

  21. Duan, L., Shi, M., Huang, L.: New esults on finite-/fixed-time synchronization of delayed diffusive fuzzy HNNs with discontinuous activations. Fuzzy Sets Syst. 416, 141–151 (2021)

    Article  Google Scholar 

  22. Lv, W., Wang, F.: Adaptive fuzzy finite-time control for uncertain nonlinear systems with aasymmetric actuator backlash. Int. J. Fuzzy Syst. 21, 50–59 (2019)

    Article  MathSciNet  Google Scholar 

  23. Tang, Y.: Terminal sliding mode control for rigid robots. Automatica 34, 51–56 (1998)

    Article  MathSciNet  Google Scholar 

  24. Hardy, G., Littlewood, J., Polya, G.: Inequalities. Cambridge University Press, London (1988)

    MATH  Google Scholar 

  25. Cui, N., Jiang, H., Hu, C., Abdurahman, A.: Finite-time synchronization of inertial neural networks. J. Assoc. Arab Univ. Basic Appl. Sci. 24, 300–309 (2017)

    Google Scholar 

  26. Tu, Z., Cao, J., Hayat, T.: Global exponential stability in Lagrange sense for inertial neural networks with time-varying delays. Neurocomputing 171, 524–531 (2016)

    Article  Google Scholar 

  27. Wang, J., Tian, L.: Global Lagrange stability for inertial neural networks with mixed time-varying delays. Neurocomputing 235, 140–146 (2017)

    Article  Google Scholar 

  28. Jian, J., Duan, L.: Finite-time synchronization for fuzzy neutral-type inertial neural networks with time-varying coefficients and proportional delays. Fuzzy Sets Syst. 381, 51–67 (2020)

    Article  MathSciNet  Google Scholar 

  29. Alimi, A., Aouiti, C., Assali, E.: Finite-time and fixed-time synchronization of a class of inertial neural networks with multi-proportional delays and its application to secure communication. Neurocomputing 332, 29–43 (2019)

    Article  Google Scholar 

  30. Hua, L., Zhong, S., Shi, K., Zhang, X.: Further results on finite-time synchronization of delayed inertial memristive neural networks via a novel analysis method. Neural Netw. 127, 47–57 (2020)

    Article  Google Scholar 

  31. Aouiti, C., Abed, A., Karray, F.: Finite time synchronization for delayed fuzzy inertial cellular neural networks. In: 2018 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp. 1–8. (2018)

Download references

Funding

This work was jointly supported by the Major Program of University Natural Science Research Fund of Anhui Province (KJ2020ZD32), China Postdoctoral Science Foundation (2018M640579), Postdoctoral Science Foundation of Anhui Province (2019B329), National Natural Science Foundation of China (11701007, 11971076).

Author information

Authors and Affiliations

  1. School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, 232001, Anhui, People’s Republic of China

    Lian Duan, Min Shi & Minglei Fang

  2. School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, Hunan, People’s Republic of China

    Chuangxia Huang

Authors
  1. Lian Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chuangxia Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Minglei Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lian Duan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Duan, L., Shi, M., Huang, C. et al. New Results on Finite-Time Synchronization of Delayed Fuzzy Neural Networks with Inertial Effects. Int. J. Fuzzy Syst. 24, 676–685 (2022). https://doi.org/10.1007/s40815-021-01171-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-021-01171-1

Keywords

Search

Navigation