Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Finite-Time and Fixed-Time Synchronization of Inertial Cohen–Grossberg-Type Neural Networks with Time Varying Delays

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

This paper is devoted to studying the finite-time and fixed-time of inertial Cohen–Grossberg type neural networks (ICGNNs) with time varying delays. First, by constructing a proper variable substitution, the original (ICGNNs) can be rewritten as first-order differential system. Second, by utilizing feedback controllers and constructing suitable Lyapunov functionals, several new sufficient conditions guaranteeing the finite-time and the fixed-time synchronization of ICGNNs with time varying delays are obtained based on different finite-time synchronization analysis techniques. The obtained sufficient conditions are simple and easy to verify. Numerical simulations are given to illustrate the effectiveness of the theoretical results.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Liu Y, Tian J, Ren Z (2017) New stability analysis for generalized neural networks with interval time-varying delays. Int J Control Autom Syst 15:1600–1610. https://doi.org/10.1007/s12555-016-0285-2

    Article  Google Scholar 

  2. Saravanakumar R, Rajchakit G, Ali M, Xiang Z, Joo Y (2017) Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2974-z

    Article  MATH  Google Scholar 

  3. Samidurai R, Rajavel S, Sriraman R, Cao J, Alsaedi A, Alsaadi FE (2017) Novel results on stability analysis of neutral-type neural networks with additive time-varying delay components and leakage delay. Int J Control Autom Syst 15(4):1888–1900

    Google Scholar 

  4. Aouiti C, Gharbia IB, Cao J, M’hamdi MS, Alsaedi A (2018) Existence and global exponential stability of pseudo almost periodic solution for neutral delay BAM neural networks with time-varying delay in leakage terms. Chaos Solitons Fractals 107:111–127

    MathSciNet  MATH  Google Scholar 

  5. Aouiti C, Dridi F (2018) New results on impulsive Cohen–Grossberg neural networks. Neural Process Lett. https://doi.org/10.1007/s11063-018-9880-y

    Article  Google Scholar 

  6. Aouiti C, Li X, Miaadi F (2018) A new LMI approach to finite and fixed time stabilization of high-order class of BAM neural networks with time-varying delays. Neural Process Lett. https://doi.org/10.1007/s11063-018-9939-9

    Article  Google Scholar 

  7. Aouiti C, M’hamdi MS, Chérif F, Alimi AM (2018) Impulsive generalized high-order recurrent neural networks with mixed delays: stability and periodicity. Neurocomputing 321:296–307

    Google Scholar 

  8. Aouiti C (2016) Neutral impulsive shunting inhibitory cellular neural networks with time-varying coefficients and leakage delays. Cognit Neurodyn 10(6):573–591

    MathSciNet  Google Scholar 

  9. Aouiti C, abed Assali E, Cao J, Alsaedi A (2018) Global exponential convergence of neutral-type competitive neural networks with multi-proportional delays, distributed delays and time-varying delay in leakage delays. Int J Syst Sci 49(10):2202–2214

    MathSciNet  Google Scholar 

  10. M’Hamdi MS, Aouiti C, Touati A, Alimi AM, Snasel V (2016) Weighted pseudo almost-periodic solutions of shunting inhibitory cellular neural networks with mixed delays. Acta Math Sci 36(6):1662–1682

    MathSciNet  MATH  Google Scholar 

  11. Cao J, Zhou D (1998) Stability analysis of delayed cellular neural networks. Neural Netw 11(9):1601–1605

    Google Scholar 

  12. Wen S, Bao G, Zeng Z, Chen Y, Huang T (2013) Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays. Neural Netw 48:195–203

    MATH  Google Scholar 

  13. He W, Cao J (2009) Exponential synchronization of chaotic neural networks: a matrix measure approach. Nonlinear Dyn 55(1–2):55–65

    MathSciNet  MATH  Google Scholar 

  14. Fang M (2015) Synchronization for complex dynamical networks with time delay and discrete-time information. Appl Math Comput 258:1–11

    MathSciNet  MATH  Google Scholar 

  15. Cao J, Wang J (2003) Global asymptotic stability of a general class of recurrent neural networks with time-varying delays. IEEE Trans Circuits Syst I Fundam Theory Appl 50(1):34–44

    MathSciNet  MATH  Google Scholar 

  16. Cao J, Yuan K, Li HX (2006) Global asymptotical stability of recurrent neural networks with multiple discrete delays and distributed delays. IEEE Trans Neural Netw 17(6):1646–1651

    Google Scholar 

  17. Cao J, Song Q (2006) Stability in Cohen–Grossberg-type bidirectional associative memory neural networks with time-varying delays. Nonlinearity 19(7):1601

    MathSciNet  MATH  Google Scholar 

  18. Cohen MA, Grossberg S (1983) Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans Syst Man Cybern 5:815–826

    MathSciNet  MATH  Google Scholar 

  19. 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(1):271–291

    Google Scholar 

  20. Zhu X, Yang X, Alsaadi FE, Hayat T (2017) Fixed-time synchronization of coupled discontinuous neural networks with nonidentical perturbations. Neural Process Lett. https://doi.org/10.1007/s11063-017-9770-8

    Article  Google Scholar 

  21. Xiong X, Tang R, Yang X (2018) Finite-time synchronization of memristive neural networks with proportional delay. Neural Process Lett. https://doi.org/10.1007/s11063-018-9910-9

    Article  Google Scholar 

  22. Zhu Q, Cao J (2010) Adaptive synchronization of chaotic Cohen–Crossberg neural networks with mixed time delays. Nonlinear Dyn 61(3):517–534

    MathSciNet  MATH  Google Scholar 

  23. Hu C, Yu J, Jiang H (2014) Finite-time synchronization of delayed neural networks with Cohen–Grossberg type based on delayed feedback control. Neurocomputing 143:90–96

    Google Scholar 

  24. Zhu Q, Cao J (2012) pth moment exponential synchronization for stochastic delayed Cohen–Grossberg neural networks with Markovian switching. Nonlinear Dyn 67(1):829–845

    MATH  Google Scholar 

  25. Cao J, Chen G, Li P (2008) Global synchronization in an array of delayed neural networks with hybrid coupling. IEEE Trans Syst Man Cybern Part B (Cybern) 38(2):488–498

    Google Scholar 

  26. Yu W, Cao J, Lu W (2010) Synchronization control of switched linearly coupled neural networks with delay. Neurocomputing 73(4–6):858–866

    Google Scholar 

  27. Lu J, Ho DW (2010) Globally exponential synchronization and synchronizability for general dynamical networks. IEEE Trans Syst Man Cybern Part B (Cybern) 40(2):350–361

    Google Scholar 

  28. Zhang W, Li C, Huang T, Huang J (2018) Fixed-time synchronization of complex networks with nonidentical nodes and stochastic noise perturbations. Phys A Stat Mech Appl 492:1531–1542

    MathSciNet  Google Scholar 

  29. Liu X, Lam J, Yu W, Chen G (2016) Finite-time consensus of multiagent systems with a switching protocol. IEEE Trans Neural Netw Learn Syst 27(4):853–862

    MathSciNet  Google Scholar 

  30. Gao J, Zhu P, Alsaedi A, Alsaadi FE, Hayat T (2017) A new switching control for finite-time synchronization of memristor-based recurrent neural networks. Neural Netw 86:1–9. https://doi.org/10.1016/j.neunet.2016.10.008

    Article  Google Scholar 

  31. Li Y, Yang X, Shi L (2016) Finite-time synchronization for competitive neural networks with mixed delays and non-identical perturbations. Neurocomputing 185:242–253

    Google Scholar 

  32. Yang X, Cao J, Lu J (2011) Synchronization of delayed complex dynamical networks with impulsive and stochastic effects. Nonlinear Anal Real World Appl 12(4):2252–2266

    MathSciNet  MATH  Google Scholar 

  33. Strogatz SH, Stewart I (1993) Coupled oscillators and biological synchronization. Sci Am 269(6):102–109

    Google Scholar 

  34. Yu J, Hu C, Jiang H, Teng Z (2011) Exponential synchronization of Cohen–Grossberg neural networks via periodically intermittent control. Neurocomputing 74(10):1776–1782

    Google Scholar 

  35. Lv T, He W, Yan P (2011) Exponential synchronization of Cohen–Grossberg neural networks with diffusion terms and delays. In: Fourth international conference on the applications of digital information and web technologies (ICADIWT), pp 65–69

  36. Abdurahman A, Jiang H, Teng Z (2017) Lag synchronization for Cohen–Grossberg neural networks with mixed time-delays via periodically intermittent control. Int J Comput Math 94(2):275–295

    MathSciNet  MATH  Google Scholar 

  37. Yang X, Ho DW, Lu J, Song Q (2015) Finite-time cluster synchronization of TS fuzzy complex networks with discontinuous subsystems and random coupling delays. IEEE Trans Fuzzy Syst 23(6):2302–2316

    Google Scholar 

  38. He X, Li C, Shu Y (2012) BogdanovTakens bifurcation in a single inertial neuron model with delay. Neurocomputing 89:193–201

    Google Scholar 

  39. He X, Yu J, Huang T, Li C, Li C (2014) Neural network for solving Nash equilibrium problem in application of multiuser power control. Neural Netw 57:73–78

    MATH  Google Scholar 

  40. Wheeler DW, Schieve WC (1997) Stability and chaos in an inertial two-neuron system. Phys D Nonlinear Phenom 105(4):267–284

    MATH  Google Scholar 

  41. Yu S, Zhang Z, Quan Z (2015) New global exponential stability conditions for inertial Cohen–Grossberg neural networks with time delays. Neurocomputing 151:1446–1454

    Google Scholar 

  42. Ke Y, Miao C (2013) Stability analysis of inertial Cohen–Grossberg-type neural networks with time delays. Neurocomputing 117:196–205

    Google Scholar 

  43. Zhang Z, Quan Z (2015) Global exponential stability via inequality technique for inertial BAM neural networks with time delays. Neurocomputing 151:1316–1326

    Google Scholar 

  44. Qi J, Li C, Huang T (2015) Stability of inertial BAM neural network with time-varying delay via impulsive control. Neurocomputing 161:162–167

    Google Scholar 

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

    Google Scholar 

  46. Huang Q, Cao J (2018) Stability analysis of inertial Cohen–Grossberg neural networks with Markovian jumping parameters. Neurocomputing 282:89–97

    Google Scholar 

  47. Wang L, Zou X (2002) Exponential stability of Cohen–Grossberg neural networks. Neural Netw 15(3):415–422

    Google Scholar 

  48. Liao X, Li C, Wong KW (2004) Criteria for exponential stability of Cohen–Grossberg neural networks. Neural Netw 17(10):1401–1414

    MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  50. Liu X, Ho DW, Song Q, Xu W (2018) Finite/fixed-time pinning synchronization of complex networks with stochastic disturbances. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2018.2821119

    Article  Google Scholar 

  51. Liu X, Cao J, Xie C (2017) Finite-time and fixed-time bipartite consensus of multi-agent systems under a unified discontinuous control protocol. J Frank Inst. https://doi.org/10.1016/j.jfranklin.2017.10.009

    Article  MATH  Google Scholar 

  52. Liu X, Ho DW, Song Q, Cao J (2017) Finite-/fixed-time robust stabilization of switched discontinuous systems with disturbances. Nonlinear Dyn 90(3):2057–2068

    MathSciNet  MATH  Google Scholar 

  53. Chen C, Li L, Peng H, Yang Y, Li T (2017) Finite-time synchronization of memristor-based neural networks with mixed delays. Neurocomputing 235:83–89

    Google Scholar 

  54. 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 

  55. Wan Y, Cao J, Wen G, Yu W (2016) Robust fixed-time synchronization of delayed Cohen–Grossberg neural networks. Neural Netw 73:86–94

    MATH  Google Scholar 

  56. Shi L, Yang X, Li Y, Feng Z (2016) Finite-time synchronization of nonidentical chaotic systems with multiple time-varying delays and bounded perturbations. Nonlinear Dyn 83(1–2):75–87

    MathSciNet  MATH  Google Scholar 

  57. Su T, Yang X (2016) Finite-time synchronization of competitive neural networks with mixed delays. Discret Contin Dyn Syst Ser B 21(10):3655–3667

    MathSciNet  MATH  Google Scholar 

  58. Yang X (2014) Can neural networks with arbitrary delays be finite-timely synchronized? Neurocomputing 143:275–281

    Google Scholar 

  59. Yang X, Song Q, Liang J, He B (2015) Finite-time synchronization of coupled discontinuous neural networks with mixed delays and nonidentical perturbations. J Frankl Inst 352(10):4382–4406

    MathSciNet  MATH  Google Scholar 

  60. Zhang W, Yang X, Xu C, Feng J, Li C (2018) Finite-time synchronization of discontinuous neural networks with delays and mismatched parameters. IEEE Trans Neural Netw Learn Syst 29(8):3761–3771

    MathSciNet  Google Scholar 

  61. Haimo VT (1986) Finite time controllers. SIAM J Control Optim 24(4):760–770

    MathSciNet  MATH  Google Scholar 

  62. Muralidharan A, Pedarsani R, Varaiya P (2015) Analysis of fixed-time control. Transp Res Part B Methodol 73:81–90

    Google Scholar 

  63. Zhu Q, Li X (2012) Exponential and almost sure exponential stability of stochastic fuzzy delayed Cohen–Grossberg neural networks. Fuzzy Sets Syst 203:74–94

    MathSciNet  MATH  Google Scholar 

  64. Zuo Z, Han QL, Ning B, Ge X, Zhang XM (2018) An overview of recent advances in fixed-time cooperative control of multiagent systems. IEEE Trans Ind Inform 14(6):2322–2334

    Google Scholar 

  65. Amato F, Ariola M, Dorato P (2001) Finite-time control of linear systems subject to parametric uncertainties and disturbances. Automatica 37(9):1459–1463

    MATH  Google Scholar 

  66. Chen T, Bai Y (2007) Stability of Cohen–Grossberg neural networks with nonnegative periodic solutions. In: International joint conference on neural networks, IJCNN 2007, pp 242–247

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chaouki Aouiti.

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

Aouiti, C., Assali, E.A. & Foutayeni, Y.E. Finite-Time and Fixed-Time Synchronization of Inertial Cohen–Grossberg-Type Neural Networks with Time Varying Delays. Neural Process Lett 50, 2407–2436 (2019). https://doi.org/10.1007/s11063-019-10018-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-019-10018-8

Keywords

Mathematics Subject Classification

Navigation