Skip to main content
Log in

Asymptotic and finite-time synchronization of memristor-based switching networks with multi-links and impulsive perturbation

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

Abstract

Based on the structure and the working mechanism of real neurons, a new mathematical model of the memristor-based switching networks (MSNs) with multiple links and time-varying delays is proposed. Further, we study the asymptotic and finite-time synchronizations of the proposed MSNs via adaptive controller and intermittent controller. Based on the stability theory and the linear matrix inequality theory, some effective asymptotic and finite-time synchronization criteria are derived to ensure the stability of the error system between the drive and response networks. Finally, numerical simulations show the effectiveness and the correctness of our results.

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

Similar content being viewed by others

References

  1. Zhao H, Li L, Peng H, Xiao J, Yang Y (2016) Finite-time boundedness analysis of memristive neural network with time-varying delay. Neural Process Lett 44:665–679

    Article  Google Scholar 

  2. Wu A, Wen S, Zeng Z (2012) Synchronization control of a class of memristor-based recurrent neural networks. Inf Sci 183:106–116

    Article  MathSciNet  MATH  Google Scholar 

  3. Chua LO (1971) Memristor-the missing circuit element. IEEE Trans Circuit Theory 18:507–519

    Article  Google Scholar 

  4. Strukov D, Snider G, Stewart D, Williams R (2008) The missing memristor found. Nature 453:80–83

    Article  Google Scholar 

  5. Guo Z, Yang S, Wang J (2016) Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control. Neural Netw 84:67–79

    Article  Google Scholar 

  6. Wang L, Shen Y, Yin Q, Zhang G (2015) Adaptive synchronization of memristor-based neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst 26:2033–2042

    Article  MathSciNet  Google Scholar 

  7. Wen S, Zeng Z, Huang T, Zhang Y (2014) Exponential adaptive lag synchronization of memristive neural networks via fuzzy method and applications in pseudorandom number generators. IEEE Trans Fuzzy Syst 22:1704–1713

    Article  Google Scholar 

  8. Wang G, Shen Y (2014) Exponential synchronization of coupled memristive neural networks with time delays. Neural Comput Appl 24:1421–1430

    Article  Google Scholar 

  9. Zhang G, Hu J, Shen Y (2015) New results on synchronization control of delayed memristive neural networks. Nonlinear Dyn 81:1167–1178

    Article  MathSciNet  MATH  Google Scholar 

  10. Ding S, Wang Z (2016) Lag quasi-synchronization for memristive neural networks with switching jumps mismatch. Neural Comput Appl 2016:1–12

    Google Scholar 

  11. Wang W, Li L, Peng H et al (2016) Anti-synchronization of coupled memristive neutral-type neural networks with mixed time-varying delays via randomly occurring control. Nonlinear Dyn 83(4):2143–2155

    Article  MathSciNet  MATH  Google Scholar 

  12. Guo Z, Yang S, Wang J (2015) Global exponential synchronization of multiple memristive neural networks with time delay via nonlinear coupling. IEEE Trans Neural Netw Learn Syst 26:1300–1311

    Article  MathSciNet  Google Scholar 

  13. Wang L, Shen Y (2015) Design of controller on synchronization of memristor-based neural networks with time-varying delays. Neurocomputing 147:372–379

    Article  Google Scholar 

  14. Han X, Wu H, Fang B (2016) Adaptive exponential synchronization of memristive neural networks with mixed time-varying delays. Neurocomputing 201:40–50

    Article  Google Scholar 

  15. Bao H, Cao J (2015) Projective synchronization of fractional-order memristor-based neural networks. Neural Netw 63:1–9

    Article  MATH  Google Scholar 

  16. Chandrasekar A, Rakkiyappan R (2016) Impulsive controller design for exponential synchronization of delayed stochastic memristor-based recurrent neural networks. Neurocomputing 173:1348–1355

    Article  Google Scholar 

  17. Mathiyalagan K, Park JH, Sakthivel R (2015) Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities. Appl Math Comput 259:967–979

    MathSciNet  MATH  Google Scholar 

  18. Zhang G, Shen Y (2015) Exponential stabilization of memristor-based chaotic neural networks with time-varying delays via intermittent control. IEEE Trans Neural Netw Learn Syst 26:1431–1441

    Article  MathSciNet  Google Scholar 

  19. Yang S, Li C, Huang T (2016) Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control. Neural Netw 75:162–172

    Article  MATH  Google Scholar 

  20. Zhang W, Li C, Huang T, Huang J (2016) Stability and synchronization of memristor-based coupling neural networks with time-varying delays via intermittent control. Neurocomputing 173:1066–1072

    Article  Google Scholar 

  21. Yang Z, Luo B, Liu D, Li Y (2017) Pinning synchronization of memristor-based neural networks with time-varying delays. Neural Netw 93:143–151

    Article  Google Scholar 

  22. Guan W, Yi S, Quan Y (2013) Exponential synchronization of coupled memristive neural networks via pinning control. Chin Phys B 22(5):203–212

    Google Scholar 

  23. Li N, Cao J (2015) New synchronization criteria for memristor-based networks: adaptive control and feedback control schemes. Neural Netw 61:1–9

    Article  MATH  Google Scholar 

  24. Zhao H, Li L, Peng H et al (2015) Anti-synchronization for stochastic memristor-based neural networks with non-modeled dynamics via adaptive control approach. Eur Phys J B 88:1–10

    MathSciNet  Google Scholar 

  25. Bao H, Ju HP, Cao J (2015) Adaptive synchronization of fractional-order memristor-based neural networks with time delay. Nonlinear Dyn 82:1–12

    Article  MathSciNet  MATH  Google Scholar 

  26. Velmurugan G, Rakkiyappan R, Cao J (2015) Finite-time synchronization of fractional-order memristor-based neural networks with time delays. Nonlinear Dyn 73:36–46

    MATH  Google Scholar 

  27. Cui X, Yu Y, Wang H et al (2016) Dynamical analysis of memristor-based fractional-order neural networks with time delay. Mod Phys Lett B 30(18):1650271. https://doi.org/10.1142/S0217984916502717

    Article  MathSciNet  Google Scholar 

  28. Chen J, Zeng Z, Jiang P (2014) On the periodic dynamics of memristor-based neural networks with time-varying delays. Inf Sci 279:358–373

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhang G, Shen Y (2013) New algebraic criteria for synchronization stability of chaotic memristive neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst 24:1701–1707

    Article  Google Scholar 

  30. Zheng M, Li L, Peng H, Xiao J, Yang Y, Zhao H (2016) Finite-time stability and synchronization for memristor-based fractional-order Cohen–Grossberg neural network. Eur Phys J B. https://doi.org/10.1140/epjb/e2016-70337-6

  31. Zhang G, Shen Y (2014) Exponential synchronization of delayed memristor-based chaotic neural networks via periodically intermittent control. Neural Netw 55:1–10

    Article  MATH  Google Scholar 

  32. Yang X, Cao J, Qiu J (2015) Pth moment exponential stochastic synchronization of coupled memristor-based neural networks with mixed delays via delayed impulsive control. Neural Netw 65:80–91

    Article  MATH  Google Scholar 

  33. Institute of Curriculum and Teaching Materials (2015) Biological compulsory course 3: the steady state and environment. People’s Education Press, Beijing

    Google Scholar 

  34. Wu A, Zeng Z, Zhu X, Zhang J (2011) Exponential synchronization of memristor-based recurrent neural networks with time delays. Neurocomputing 74:3043–3050

    Article  Google Scholar 

  35. Wu A, Zeng Z (2012) Exponential stabilization of memristive neural networks with time delays. IEEE Trans Neural Netw Learn Syst 23:1919–1929

    Article  Google Scholar 

  36. Abdurahman A, Jiang H, Teng Z (2015) Finite-time synchronization for memristor-based neural networks with time-varying delays. Neural Netw 69:20–28

    Article  MATH  Google Scholar 

  37. Boyd S, Ghaoui LE, Feron E, Balakrishnan V (1994) Linear matrix inequalities in system and control theory. SIAM, Philadelphia

    Book  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  39. Mei J, Jiang M, Wang B, Long B (2013) Finite-time parameter identification and adaptive synchronization between two chaotic neural networks. J Frankl Inst 350:1617–1633

    Article  MathSciNet  MATH  Google Scholar 

  40. Wang J, Jian J, Yan P (2009) Finite-time boundedness analysis of a class of neutral type neural networks with time delays. Advances in Neural Networks-ISNN 2009. Part I, LNCS 5551:395–404

Download references

Acknowledgements

The authors would like to thank all the editor, associate editor as well as the anonymous reviewers for their constructive suggestions and valuable comments, which are important and helpful to improve the quality of this paper. The work is supported by the National Key Research and Development Program (Grant No. 2016YFB0800602) and the National Natural Science Foundation of China (Grant Nos. 61472045, 61573067, 61771071).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lixiang Li.

Ethics declarations

Conflict of interest

All the authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiu, B., Li, L., Peng, H. et al. Asymptotic and finite-time synchronization of memristor-based switching networks with multi-links and impulsive perturbation. Neural Comput & Applic 31, 4031–4047 (2019). https://doi.org/10.1007/s00521-017-3312-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-3312-1

Keywords

Navigation