Skip to main content
Log in

New Delay-Dependent Stability Criteria for Impulsive Neural Networks with Additive Time-Varying Delay Components and Leakage Term

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

This work is concerned with the delay-dependent stability problem for uncertain impulsive neural networks (NNs) with additive time-varying delay components and leakage term. We construct a newly augmented Lyapunov–Krasovskii (L–K) functional which contains triple and four integral terms and then utilizing free-matrix-based integral inequality to bound the derivative of the Lyapunov–Krasovskii functional. Some sufficient conditions are derived to assure the delay-dependent stability of the impulsive NNs by the linear matrix inequality, which is less conservative than some existing results and can be readily verified by the convex optimization algorithms. In addition, some information of activation function ignored in previous works has been taken into account in the resulting condition. In the end, three numerical examples are provided to illustrate the effectiveness of the proposed criteria.

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.

Similar content being viewed by others

References

  1. Haykin S (1998) Neural networks: a comprehensive foundation. Prentice-Hall, Upper Saddle River

    MATH  Google Scholar 

  2. Cohen M, Grossberg S (1983) Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans Syst Man Cybern 13:815–826

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  4. Cichocki A, Unbehauen R (1993) Neural networks for optimization and signal processing. Wiley, Chichester

    MATH  Google Scholar 

  5. Forti M, Tesi A (1995) New conditions for global stability of neural networks with application to linear and quadratic programming problems. IEEE Trans Circuits Syst I(42):354–365

    Article  MathSciNet  MATH  Google Scholar 

  6. Tu Z, Cao J, Alsaedi A, Alsaadi FE, Hayat T (2016) Global Lagrange stability of complex-valued neural networks of neutral type with time-varying delays. Complexity 21:438–450

    Article  MathSciNet  Google Scholar 

  7. Pan L, Cao J (2012) Exponential stability of stochastic functional differential equations with Markovian switching and delayed impulses via Razumikhin method. Adv Differ Equ 2012:61

    Article  MathSciNet  MATH  Google Scholar 

  8. Liu X, Chen T (2002) A new result on the global convergence of Hopfield neural networks. IEEE Trans Circuits Syst I(49):1514–1516

    MathSciNet  MATH  Google Scholar 

  9. Base AM, Roberts R, Yu HG (2007) Robust stability analysis of competitive neural networks with different time-scals under perturbations. Neurocomputing 71:417–420

    Article  Google Scholar 

  10. Chen SS (2011) Chaotic simulated annealing by a neural network with avariable delay: design and application. IEEE Trans Neural Netw 22:1557–1565

    Article  Google Scholar 

  11. Baldi P, Atiya AF (1994) How delays affect neural dynamics and learning. IEEE Trans Neural Netw 5:612–621

    Article  Google Scholar 

  12. Arik S (2002) Global asymptotic stability of a larger class of neural networks with constant time delay. Phys Lett A 311:504–511

    Article  MATH  Google Scholar 

  13. Cao J (2000) Global asymptotic stability of neural networks with transmission delays. Int J Syst Sci 31:1313–1316

    Article  MATH  Google Scholar 

  14. Chen T, Rong L (2003) Delay-independent stability analysis of Cohen–Grossberg neural networks. Phys Lett A 317:436–499

    Article  MathSciNet  MATH  Google Scholar 

  15. He Y, Wu M, She JH (2006) Delay-dependent exponential stability of delayed neural networks with time-varying delay. IEEE Trans Circuits Syst I(53):553–557

    Google Scholar 

  16. Yang QF, Ren QH, Xie X (2014) New delay dependent stability criteria for recurrent neural networks with interval time-varying delay. ISA Trans 53:994–999

    Article  Google Scholar 

  17. Raja R, Samidurai R (2012) New delay dependent robust asymptotic stability for uncertain stochastic recurrent neural networks with multiple time varying delays. J Frankl Inst 349:2108–2123

    Article  MathSciNet  MATH  Google Scholar 

  18. Wei RY, Cao JD (2018) Synchronization analysis of inertial memristive neural networks with time-varying delays. J Artif Intell Soft Comput Res 8(4):269–282

    Article  Google Scholar 

  19. Li R, Cao J (2016) Stability analysis of reaction–diffusion uncertain memristive neural networks with time-varying delays and leakage term. Appl Math Comput 278:54–69

    MathSciNet  MATH  Google Scholar 

  20. Tu Z, Cao J, Hayat T (2016) Matrix measure based dissipativity analysis for inertial delayed uncertain neural networks. Neural Netw 75:47–55

    Article  Google Scholar 

  21. Kwon OM, Park JH, Lee SM (2008) On robust stability for uncertain neural networks with interval time-varying delays. IET Control Theory Appl 2:625–634

    Article  MathSciNet  Google Scholar 

  22. Gopalsamy K (2007) Leakage delays in BAM. J Math Anal Appl 325(2):1117–1132

    Article  MathSciNet  MATH  Google Scholar 

  23. Gopalsamy K (1992) Stabiltiy and oscillations in delay differential equations of population dynamics. Kluwer Academic Publishers, Dordrecht

    Book  MATH  Google Scholar 

  24. Li X, Cao J (2010) Delay-dependent stability of neural networks of neutral type with time delay in the leakage term. Nonlinearity 23:1709–1726

    Article  MathSciNet  MATH  Google Scholar 

  25. Liu B (2013) Global exponential stability for BAM neural networks with time-varying delays in the leakage terms. Nonlinear Anal Real World Appl 14:559–566

    Article  MathSciNet  MATH  Google Scholar 

  26. Li R, Cao J (2015) Stability analysis of reaction–diffusion uncertain memristive neural networks with time-varying delays and leakage term. Appl Math Comput 278:54–69

    Article  MathSciNet  MATH  Google Scholar 

  27. Samidurai R, Rajavel S, Zhu Q, Raja R, Zhou H (2016) Robust passavity analysis for neutral-type of neural networks with mixed and leakage delays. Neurocomputing 175:635–643

    Article  Google Scholar 

  28. Zhang G, Liu Z, Ma Z (2007) Synchronization of complex dynamical networks via impulsive control. Chaos 17:043126

    Article  MathSciNet  MATH  Google Scholar 

  29. Yao F, Cao J, Cheng P, Qiu L (2016) Generalized average dwell time approach to stability and input-to-state stability of hybrid impulsive stochastic differential systems. Nonlinear Anal Hybrid 22:147–160

    Article  MathSciNet  MATH  Google Scholar 

  30. Li Y (2017) Impulsive synchronization of stochastic neural networks via controlling partial states. Neural Process Lett 46:59–69

    Article  Google Scholar 

  31. Lu J, Wang Z, Cao J, Ho DWC, Kurths J (2012) Pinning impulsive stabilization of nonlinear dynamical networks with time-varying delay. Int J Bifurcat Chaos 22:1250176

    Article  MATH  Google Scholar 

  32. Lu J, Ding C, Lou J, Cao J (2015) Outer synchronization of partially coupled dynamical networks via pinning impulsive controllers. J Frankl Inst 352:5024–5041

    Article  MathSciNet  MATH  Google Scholar 

  33. Wang Q, Liu X (2005) Exponential stability for impulsive delay differential equations by Razumikhinmethod. J Math Anal Appl 309:462–473

    Article  MathSciNet  MATH  Google Scholar 

  34. Li D, Yang D, Wang H, Zhang X, Wang S (2009) Asymptotical stability of multi-delayed cellular neural networks with impulsive effects. Phys Lett A 388:218–224

    Google Scholar 

  35. Aouiti C (2018) Oscillation of impulsive neutral delay generalized high-order Hopfield neural networks. Neural Comput Appl 29:477–495

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  37. Aouiti C, Mhamdi MS, Cao J, Alsaedi A (2017) Pseudo almost periodic solution for impulsive generalised high-order Hopfield neural networks with leakage delays. Neural Process Lett 45(2):615–648

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  39. Hu W, Li C, Wu S (2012) Stochastic robust stability for neutral-type impulsive interval neural networks with distributed time-varying delays. Neural Comput Appl 21:1947–1960

    Article  Google Scholar 

  40. Raja R, Zhu Q, Senthilraj S, Samidurai R (2015) Improved stability analysis of uncertain neural type neural networks with leakage delays and impulsive effects. Appl Math Comput 266:1050–1069

    MathSciNet  MATH  Google Scholar 

  41. Zhao Y, Gao H, Mou S (2008) Asymptotic stability analysis of neural networks with successive time delay components. Neurocomputing 71:2848–2856

    Article  Google Scholar 

  42. Shao H, Han QL (2011) New delay-dependent stability criteria for neural networks with two additive time-varying delay components. IEEE Trans Neural Netw 22:812–818

    Article  Google Scholar 

  43. Xiao N, Jia Y (2013) New approaches on stability criteria for neural networks with two additive time-varying delay components. Neurocomputing 118:150–156

    Article  Google Scholar 

  44. Tian J, Zhong S (2012) Improved delay-dependent stability criteria for neural networks with two additive time-varying delay components. Neurocomputing 77:114–119

    Article  Google Scholar 

  45. Dharani S, Rakkiyappan R, Cao J (2015) New delay-dependent stability criteria for switched Hopfield neural networks of neutral type with additive time-varying delay components. Neurocomputing 151:827–834

    Article  Google Scholar 

  46. Zhang C, He Y, Jiang L, Wu QH, Wu M (2014) Delay-dependent stability criteria for generalized neural networks with two delay components. IEEE Trans Neural Netw Learn Syst 25:1263–1276

    Article  Google Scholar 

  47. Rakkiyappan R, Sivasamy R, Park JH, Lee TH (2016) An improved stability criterion for generalized neural networks with additive time-varying delays. Neurocomputing 171:615–624

    Article  Google Scholar 

  48. Liu Y, Lee SM, Lee HG (2015) Robust delay-dependent stability criteria for uncertain neural networks with two additive time-varying delay components. Neurocomputing 151:770–775

    Article  Google Scholar 

  49. Kwon OM, Park JH, Lee SM, Cha EJ (2014) New augmented Lyapunov–Krasovskii functional approach to stability analysis of neural networks with time-varying delays. Nonlinear Dyn 76:221–236

    Article  MathSciNet  MATH  Google Scholar 

  50. Zeng HB, He Y, Wu M, She J (2015) Free-matrix-based integral inequality for stability analysis of systems with time-varying delay. IEEE Trans Autom Control 60:2768–2772

    Article  MathSciNet  MATH  Google Scholar 

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

    Book  MATH  Google Scholar 

  52. Xie L (1996) Output feedback \(H_\infty \) control of systems with parameter uncertainty. Int J Control 63:741–750

    Article  MathSciNet  MATH  Google Scholar 

  53. Lou X, Ye Q, Cui B (2010) Exponential stability of genetic regulatory networks with random delays. Neurocomputing 73:759–769

    Article  Google Scholar 

Download references

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.

This work was supported by Science and Engineering Research Board , New Delhi, India, under the Sanctioned No. SB/EMEQ-181/2013, the National Natural Science Foundation of China under Grant Nos. 61573096 and 61272530, and the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence under Grant No. BM2017002.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Samidurai, R., Rajavel, S., Cao, J. et al. New Delay-Dependent Stability Criteria for Impulsive Neural Networks with Additive Time-Varying Delay Components and Leakage Term. Neural Process Lett 49, 761–785 (2019). https://doi.org/10.1007/s11063-018-9855-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9855-z

Keywords

Navigation