Skip to main content
Log in

On Impulsive Synchronization Control for Coupled Inertial Neural Networks with Pinning Control

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

The impulsive control for the synchronization problem of coupled inertial neural networks involved distributed-delay coupling is investigated in the present paper. A novel impulsive pinning control method is introduced to obtain the complete synchronization of the coupled inertial neural networks with three different coupling structures. At each impulsive control instant, the pinning-controlled nodes can be selected according to our selection strategy which is dependent on the lower bound of the pinning control ratio. Our criteria can be utilized to declare the synchronization of the coupled neural networks with asymmetric and reducible coupling structures. The effectiveness of our control strategy is exhibited by typical numerical examples.

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.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Yu T, Cao D, Liu S, Chen H (2016) Stability analysis of neural networks with periodic coefficients and piecewise constant arguments. J Frankl Inst 353:409–425

    MathSciNet  MATH  Google Scholar 

  2. Wen S, Zeng Z, Chen MZQ, 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

    MathSciNet  Google Scholar 

  3. Cao J, Manivannan R, Chong KT, Lv X (2019) Enhanced L2–L\(\infty \) state estimation design for delayed neural networks including leakage term via quadratic-type generalized free-matrix-based integral inequality. J Frank Inst 356(13):7371–7392

    MATH  Google Scholar 

  4. Hu C, Yu J, Chen Z, Jiang H, Huang T (2017) Fixed-time stability of dynamical systems and fixed-time synchronization of coupled discontinuous neural networks. Neural Netw 89:74–83

    MATH  Google Scholar 

  5. Pershin YV, Ventra MD (2010) Experimental demonstration of associative memory with memristive neural networks. Neural Netw 23:881–886

    Google Scholar 

  6. Qi J, Li C, Huang T (2014) Stability of delayed memristive neural networks with time-varying impulses. Cogn Neurodyn 8(5):429–436

    Google Scholar 

  7. Jiang F, Shen Y (2013) Stability of stochastic \(\theta \)-methods for stochastic delay Hopfield neural networks under regime switching. Neural Process Lett 38(3):433–444

    Google Scholar 

  8. Ali MS, Saravanakumar R, Ahn CK, Karimi HR (2017) Stochastic \(H_{\infty }\) filtering for neural networks with leakage delay and mixed time-varying delays. Inf Sci 388–399:118–134

    MATH  Google Scholar 

  9. Cao Y, Samidurai R, Sriraman R (2019) Stability and dissipativity analysis for neutral type stochastic Markovian jump static neural networks with time delays. J Artif Intell Soft Comput Res 9(3):189–204

    Google Scholar 

  10. Babcock KL, Westervelt RM (1987) Dynamics of simple electronic neural networks. Physica D 28(3):464–469

    MathSciNet  Google Scholar 

  11. Yu T, Wang H, Su M, Cao D (2018) Distributed-delay-dependent exponential stability of impulsive neural networks with inertial term. Neurocomputing 313:220–228

    Google Scholar 

  12. Wang L, Zeng Z, Ge MF, Hu J (2018) Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays. Neural Netw 105:65–74

    MATH  Google Scholar 

  13. Maharajan C, Raja R, Cao J, Rajchakit G (2018) Novel global robust exponential stability criterion for uncertain inertial-type BAM neural networks with discrete and distributed time-varying delays via Lagrange sense. J Frankl Inst 355:4727–4754

    MathSciNet  MATH  Google Scholar 

  14. Zhang W, Huang T, Li C, Yang J (2018) Robust stability of inertial BAM neural networks with time delays and uncertainties via impulsive effect. Neural Process Lett 48(1):245–256

    Google Scholar 

  15. Huang C, Zhang H, Cao J, Hu H (2019) Stability and Hopf bifurcation of a delayed prey-predator model with disease in the predator. Int J Bifurc Chaos 29(7):1950091

    MathSciNet  MATH  Google Scholar 

  16. Zhang G, Zeng Z, Hu J (2018) New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays. Neural Netw 97:183–191

    MATH  Google Scholar 

  17. Tu Z, Cao J, Alsaedi A, Alsaadi F (2017) Global dissipativity of memristor-based neutral type inertial neural networks. Neural Netw 88:125–133

    MATH  Google Scholar 

  18. Li H, Li C, Zhang W, Jiang X (2018) Global dissipativity of inertial neural networks with proportional delay via new generalized Halanay inequalities. Neural Process Lett 48(3):1543–1561

    Google Scholar 

  19. Yang D, Li X, Qiu J (2019) Output tracking control of delayed switched systems via state-dependent switching and dynamic output feedback. Nonlinear Anal Hybrid Syst 32:294–305

    MathSciNet  MATH  Google Scholar 

  20. Gong S, Yang S, Guo Z, Huang T (2018) Global exponential synchronization of inertial memristive neural networks with time-varying delay via nonlinear controller. Neural Netw 102:138–148

    MATH  Google Scholar 

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

    Google Scholar 

  22. Rakkiyappan R, Kumari EU, Chandrasekar A, Krishnasamy R (2016) Synchronization and periodicity of coupled inertial memristive neural networks with supremums. Neurocomputing 214:739–749

    Google Scholar 

  23. Rakkiyappan R, Premalatha S, Chandrasekar A, Cao J (2016) Stability and synchronization analysis of inertial memristive neural networks with time delays. Cogn Neurodyn 10(5):437–451

    Google Scholar 

  24. Zhang Y, Liu Y (2020) Nonlinear second-order multi-agent systems subject to antagonistic interactions without velocity constraints. Appl Math Comput 364:124667

    MathSciNet  MATH  Google Scholar 

  25. Li B, Lu J, Zhong J, Liu Y (2018) Fast-time stability of temporal Boolean networks. IEEE Trans Neural Netw Learn Syst 30(8):2285–2294

    MathSciNet  Google Scholar 

  26. Zhang W, Zuo Z, Wang Y, Zhang Z (2019) Double-integrator dynamics for multiagent systems with antagonistic reciprocity. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2019.2939487

    Article  Google Scholar 

  27. Zhong J, Liu Y, Kou KI, Sun L, Cao J (2019) On the ensemble controllability of Boolean control networks using STP method. Appl Math Comput 358:51–62

    MathSciNet  MATH  Google Scholar 

  28. Yang X, Li X, Lu J, Cheng Z (2019) Synchronization of time-delayed complex networks with switching topology via hybrid actuator fault and impulsive effects control. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2019.2938217

    Article  Google Scholar 

  29. Zhang L, Yang X, Xu C, Feng J (2017) Exponential synchronization of complex-valued complex networks with time-varying delays and stochastic perturbations via time-delayed impulsive control. Appl Math Comput 306:22–30

    MathSciNet  MATH  Google Scholar 

  30. Yang X, Lu J, Ho DWC, Song Q (2018) Synchronization of uncertain hybrid switching and impulsive complex networks. Appl Math Model 59:379–392

    MathSciNet  MATH  Google Scholar 

  31. Yang J, Lu J, Lou J, Liu Y (2020) Synchronization of drive-response Boolean control networks with impulsive disturbances. Appl Math Comput 364:124679

    MathSciNet  MATH  Google Scholar 

  32. Lakshmanan S, Prakash M, Lim CP, Rakkiyappan R, Balasubramaniam P, Nahavandi S (2016) Synchronization of an inertial neural network with time-varying delays and its application to secure communication. IEEE Trans Neural Netw Learn Syst 29(1):195–207

    MathSciNet  Google Scholar 

  33. Wei R, Cao J, Alsaedi A (2018) Finite-time and fixed-time synchronization analysis of inertial memristive neural networks with time-varying delays. Cogn Neurodyn 12(1):121–134

    Google Scholar 

  34. Huang D, Jiang M, Jian J (2017) Finite-time synchronization of inertial memristive neural networks with time-varying delays via sampled-date control. Neurocomputing 266:527–539

    Google Scholar 

  35. Wang Y, Lu J, Liang J, Cao J, Perc M (2018) Pinning synchronization of nonlinear coupled Lure networks under hybrid impulses. IEEE Trans Circuits Syst II Exp Briefs 66(3):432–436

    Google Scholar 

  36. Li B, Lu J, Liu Y, Wu ZG (2019) The outputs robustness of Boolean control networks via pinning control. IEEE Trans Control Netw Syst. https://doi.org/10.1109/TCNS.2019.2913543

    Article  Google Scholar 

  37. Liu Y, Li B, Lu J, Cao J (2017) Pinning control for the disturbance decoupling problem of Boolean networks. IEEE Trans Autom Control 62(12):6595–6601

    MathSciNet  MATH  Google Scholar 

  38. Li Y, Lou J, Wang Z, Alsaadi FE (2018) Synchronization of dynamical networks with nonlinearly coupling function under hybrid pinning impulsive controllers. J Frankl Inst 355(14):6520–6530

    MathSciNet  MATH  Google Scholar 

  39. He W, Qian F, Cao J (2017) Pinning-controlled synchronization of delayed neural networks with distributed-delay coupling via impulsive control. Neural Netw 85:1–9

    MATH  Google Scholar 

  40. Lu J, Ho DWC, Cao J (2010) A unified synchronization criterion for impulsive dynamical networks. Automatica 46:1215–1221

    MathSciNet  MATH  Google Scholar 

  41. Yi C, Feng J, Wang J, Xu C, Zhao Y (2017) Synchronization of delayed neural networks with hybrid coupling via partial mixed pinning impulsive control. Appl Math Comput 312:78–90

    MathSciNet  MATH  Google Scholar 

  42. Yang X, Lu J (2016) Finite-time Synchronization of coupled networks with Markovian topology and impulsive effects. IEEE Trans Autom Control 61(8):2256–2261

    MathSciNet  MATH  Google Scholar 

  43. Zhou C, Zhang W, Yang X, Xu C, Feng J (2017) Finite-time synchronization of complex-valued neural networks with mixed delays and uncertain perturbations. Neural Process Lett 46:271–291

    Google Scholar 

  44. Yang X, Lam J, Ho DWC, Feng Z (2017) Fixed-time synchronization of complex networks with impulsive effects via non-chattering control. IEEE Trans Autom Control 62(11):5511–5521

    MATH  Google Scholar 

  45. Yang X, Cao J, Xu C, Feng J (2018) Finite-time stabilization of switched dynamical networks with quantized couplings via quantized controller. Sci China Technol Sci 61(2):299–308

    Google Scholar 

Download references

Acknowledgements

Tianhu Yu was supported by National Natural Science Foundation of China (No. 11902137) and China Postdoctoral Science Foundation (No. 2019M651633); Huamin Wang was supported by National Nature Science Foundation of China(Grant Nos. 61503175, U1804158) and Science and Technology Department Program of Henan Province (Grant No. 172102210407); Jinde Cao was supported by Key Project of Natural Science Foundation of China (No. 61833005); Yang Yang is supported by National Natural Science Foundation of China (No. 11702228).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jinde Cao.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, T., Wang, H., Cao, J. et al. On Impulsive Synchronization Control for Coupled Inertial Neural Networks with Pinning Control. Neural Process Lett 51, 2195–2210 (2020). https://doi.org/10.1007/s11063-019-10189-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-019-10189-4

Keywords

Navigation