Skip to main content
Log in

Design of Controllers for Finite-Time Robust Stabilization of Inertial Delayed Neural Networks with External Disturbances

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

This paper studies the problem of finite-time robust stabilization of inertial delayed neural networks with external disturbances. The finite-time stability research of inertial neural networks can be applied to important fields such as secure communication, so it has great significant research value. However, up to now, there are few previous studies on the finite-time stability of inertial neural networks, thus this paper makes up for this gap. Based on the actual communication networks, we improve the model of inertial neural networks, adding uncertainties and external disturbances. Unlike many previous papers based on scalar sign function, this paper introduces vector sign function, combines the constructed Lyapunov function, some inequality conditions, and related lemmas to design two effective controllers composed of \(U_1(t)\) and \(U_2(t)\), which can handle uncertainties and external disturbances of the neural networks well, and realize finite-time robust stability of the neural networks with external disturbances. In addition, the theoretical part of this paper estimates an upper bound on the settling time for the system to reach stability. Under the conditions of a certain strategy, we further optimize the extremes of the settling time so that the system reaches stability in a shorter time. Our results improve and extend some recent works. Finally, two examples are given to verify the validity and correctness of the designed controllers by numerical simulations using MATLAB tool.

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

Similar content being viewed by others

References

  1. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554–2558

    MathSciNet  MATH  Google Scholar 

  2. Michel AN, Farrell JA, Sun H-F (1990) Analysis and synthesis techniques for Hopfield type synchronous discrete time neural networks with application to associative memory. IEEE Trans Circuits Syst 37(11):1356–1366

    MathSciNet  Google Scholar 

  3. Wang Z, Ding S, Shan Q, Zhang H (2017) Stability of recurrent neural networks with time-varying delay via flexible terminal method. IEEE Trans Neural Netw Learn Syst 28(10):2456–2463

    MathSciNet  Google Scholar 

  4. Abd Elaziz M, Dahou A, Abualigah L et al (2021) Advanced metaheuristic optimization techniques in applications of deep neural networks: a review. Neural Comput Appl 33(21):14079–14099

    Google Scholar 

  5. Mehrabi M, Moayedi H (2021) Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms. Environ Earth Sci 80(24):1–20

    Google Scholar 

  6. Liu Qingshan, Wang Jun (2015) \(L_1\)-minimization algorithms for sparse signal reconstruction based on a projection neural network. IEEE Trans Neural Netw Learn Syst 27(3):698–707

    Google Scholar 

  7. Tu Z, Zhao Y, Ding N, Feng Y, Zhang W (2019) Stability analysis of quaternion-valued neural networks with both discrete and distributed delays. Appl Math Comput 343:342–353

    MathSciNet  MATH  Google Scholar 

  8. Shen JC, Ma D, Gu ZH et al (2016) Darwin: a neuromorphic hardware co-processor based on spiking neural networks. Sci China Inf Sci 59:023401

    Google Scholar 

  9. Cao J, Li R (2017) Fixed-time synchronization of delayed memristor-based recurrent neural networks. Sci China Inf Sci 60(3):032201

    MathSciNet  Google Scholar 

  10. Jiang B, Lou J, Lu J et al (2021) Synchronization of chaotic neural networks: average-delay impulsive control. IEEE Trans Neural Netw Learn Syst

  11. Babcock KL, Westervelt RM (1986) Stability and dynamics of simple electronic neural networks with added inertia. Physica D 23:464–469

    Google Scholar 

  12. Huang C, Liu B (2019) New studies on dynamic analysis of inertial neural networks involving non-reduced order method. Neurocomputing 325:283–287

    Google Scholar 

  13. 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 

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

    Google Scholar 

  15. Cui N, Jiang H, Hu C et al (2018) Global asymptotic and robust stability of inertial neural networks with proportional delays. Neurocomputing 272:326–333

    Google Scholar 

  16. Xiao Q, Huang Z, Zeng Z (2017) Passivity analysis for memristor-based inertial neural networks with discrete and distributed delays. IEEE Trans Syst Man Cybern Syst 49(2):375–385

    Google Scholar 

  17. Kong F, Zhu Q, Huang T (2020) New fixed-time stability lemmas and applications to the discontinuous fuzzy inertial neural networks. IEEE Trans Fuzzy Syst

  18. Wang W, Chen W (2020) Mean-square exponential stability of stochastic inertial neural networks. Int J Control 1–7

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

    Google Scholar 

  20. Jiang B, Lu J, Liu Y (2020) Exponential stability of delayed systems with average-delay impulses. SIAM J Control Optim 58(6):3763–3784

    MathSciNet  MATH  Google Scholar 

  21. Long C, Zhang G, Zeng Z (2020) Novel results on finite-time stabilization of state-based switched chaotic inertial neural networks with distributed delays. Neural Netw 129:193–202

    MATH  Google Scholar 

  22. Xiao SP, Lian HH, Zeng HB et al (2017) Analysis on robust passivity of uncertain neural networks with time-varying delays via free-matrix-based integral inequality. Int J Control Autom Syst 15(5):2385–2394

    Google Scholar 

  23. Chen Z, Wang X, Zhong S et al (2017) Improved delay-dependent robust passivity criteria for uncertain neural networks with discrete and distributed delays. Chaos Solitons Fractals 103:23–32

    MathSciNet  MATH  Google Scholar 

  24. Chanthorn P, Rajchakit G, Kaewmesri P et al (2020) A delay-dividing approach to robust stability of uncertain stochastic complex-valued hopfield delayed neural networks. Symmetry 12(5):683

    Google Scholar 

  25. Li X, Song S, Wu J (2019) Exponential stability of nonlinear systems with delayed impulses and applications. IEEE Trans Autom Control 64(10):4024–4034

    MathSciNet  MATH  Google Scholar 

  26. Li X, Li P (2021) Stability of time-delay systems with impulsive control involving stabilizing delays. Automatica 124:109336

    MathSciNet  MATH  Google Scholar 

  27. Chen L, Huang T, Machado JAT et al (2019) Delay-dependent criterion for asymptotic stability of a class of fractional-order memristive neural networks with time-varying delays. Neural Netw 118:289–299

    MATH  Google Scholar 

  28. Xu Y, Yu J, Li W et al (2021) Global asymptotic stability of fractional-order competitive neural networks with multiple time-varying-delay links. Appl Math Comput 389:125498

    MathSciNet  MATH  Google Scholar 

  29. Chen J, Li C, Yang X (2018) Asymptotic stability of delayed fractional-order fuzzy neural networks with impulse effects. J Frankl Inst 355(15):7595–7608

    MathSciNet  MATH  Google Scholar 

  30. Yang X, Li X (2021) Finite-time stability of nonlinear impulsive systems with applications to neural networks. IEEE Trans Neural Netw Learn Syst

  31. Yang X, Li X, Cao J (2018) Robust finite-time stability of singular nonlinear systems with interval time-varying delay. J Franklin Inst 355(3):1241–1258

    MathSciNet  MATH  Google Scholar 

  32. Xu C, Li P (2019) On finite-time stability for fractional-order neural networks with proportional delays. Neural Process Lett 50(2):1241–1256

    Google Scholar 

  33. Hu J, Sui G, Du S et al (2017) Finite-time stability of uncertain nonlinear systems with time-varying delay. Math Probl Eng 2017

  34. Zhang X, Li X, Cao J et al (2018) Design of memory controllers for finite-time stabilization of delayed neural networks with uncertainty. J Frankl Inst 355(13):5394–5413

    MathSciNet  MATH  Google Scholar 

  35. Pratap A, Raja R, Cao J et al (2018) Further synchronization in finite time analysis for time-varying delayed fractional order memristive competitive neural networks with leakage delay. Neurocomputing 317:110–126

    Google Scholar 

  36. Vadivel R, Hammachukiattikul P, Rajchakit G et al (2021) Finite-time event-triggered approach for recurrent neural networks with leakage term and its application. Math Comput Simul 182:765–790

    MathSciNet  MATH  Google Scholar 

  37. Rajchakit G, Sriraman R, Lim CP et al (2021) Synchronization in finite-time analysis of Clifford-valued neural networks with finite-time distributed delays. Mathematics 9(11):1163

    Google Scholar 

  38. Pratap A, Raja R, Alzabut J et al (2020) Finite-time Mittag-Leffler stability of fractional-order quaternion-valued memristive neural networks with impulses. Neural Process Lett 51(2):1485–1526

    Google Scholar 

  39. Saravanan S, Syed Ali M, Rajchakit G et al (2021) Finite-time stability analysis of switched genetic regulatory networks with time-varying delays via Wirtinger’s integral inequality. Complexity

  40. Niamsup P, Ratchagit K, Phat VN (2015) Novel criteria for finite-time stabilization and guaranteed cost control of delayed neural networks. Neurocomputing 160:281–286

    Google Scholar 

  41. Narayanan G, Ali MS, Alam MI et al (2021) Adaptive fuzzy feedback controller design for finite-time Mittag–Leffler synchronization of fractional-order quaternion-valued reaction-diffusion fuzzy molecular modeling of delayed neural networks. IEEE Access 9:130862–130883

    Google Scholar 

  42. Boonsatit N, Sriraman R, Rojsiraphisal T et al (2021) Finite-time synchronization of Clifford-valued neural networks with infinite distributed delays and impulses. IEEE Access 9:111050–111061

    Google Scholar 

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

    MATH  Google Scholar 

  44. Lakshmanan S, Prakash M, Lim CP et al (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 

  45. Alimi AM, Aouiti C, Assali EA (2019) 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

    Google Scholar 

  46. Liu X, Ho DWC, Yu W et al (2014) A new switching design to finite-time stabilization of nonlinear systems with applications to neural networks. Neural Netw 57:94–102

    MATH  Google Scholar 

  47. Cui N, Jiang H, Hu C et al (2018) Global asymptotic and robust stability of inertial neural networks with proportional delays. Neurocomputing 272:326–333

    Google Scholar 

  48. Liu M, Jiang H, Hu C (2017) Finite-time synchronization of delayed dynamical networks via aperiodically intermittent control. J Frankl Inst 354(13):5374–5397

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Zhang.

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

Hong, N., Zhang, W., Zhou, Z. et al. Design of Controllers for Finite-Time Robust Stabilization of Inertial Delayed Neural Networks with External Disturbances. Neural Process Lett 55, 9387–9408 (2023). https://doi.org/10.1007/s11063-023-11206-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-023-11206-3

Keywords

Navigation