Skip to main content

Stability of Impulsive Cohen-Grossberg Neural Networks with Delays

  • Conference paper
Advances in Neural Networks - ISNN 2010 (ISNN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6063))

Included in the following conference series:

  • 1781 Accesses

Abstract

In this paper, with assuming global Lipschitz conditions on the activation functions, applying idea of vector Lyapunov function, Young inequality and Halanay differential inequality with delay, the global exponential stability of the equilibrium point for a class of Cohen-Grossberg neural networks with time-varying delays and impulses is investigated, the sufficient conditions for globally exponential stability of neural networks are obtained.

This work was supported by the National Natural Science Foundation of China under Grants 50775075 and the State Key Laboratory Foundation under Grants VSN-2008-01.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Jiang, L.: Stability of Cohen-Grossberg Neural Networks with Time-variable Delay. Chinese Journal of Engineering Mathematics 26(2), 243–250 (2009)

    Google Scholar 

  2. Cao, J.D., Wang, J.: Global exponential stability and periodicity of recurrent neural networks with time delay. IEEE Trans. Circuits and Systems 152, 920–931 (2005)

    MathSciNet  Google Scholar 

  3. Forti, M., Tesi, A.: New conditions for global stability of neural networks with application to linear and quadratic programing problems. IEEE Trans. Circuits Syst. I: Fund. Theor. Appl. 42, 354–366 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  4. Hardy, G.H., Littlewood, J.E., Polya, G.: Inequalities. Cambridge University Press, London (1952)

    MATH  Google Scholar 

  5. Mohamad, S., Gopalsamy, K., Akca, H.: Exponential stability of artificial neural networks with distributed delays and large impulses. Nonlinear Analysis: Real World Applications 9, 872–888 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  6. Yang, T.: Impulsive systems and control: Theory and Applications. Nova science publishers, Huntington (2001)

    Google Scholar 

  7. Akca, H., Alassar, R., Covacheva, V., Ai-Zahrani, E.: Continuous-time additive Hopfield-type neural networks with impulses. J. Math. Anal. Appl. 290, 436–451 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  8. Gopalsamy, K.: Stability of artificial neural networks with impulses. Appl. Math. & Comp. 154, 783–813 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  9. Xia, Y.H., Cao, J.D., Cheng, S.: Global exponential stability of delayed cellular neural networks with impulses. Neurocomputing 70, 2495–2501 (2007)

    Article  Google Scholar 

  10. Yang, Z.C., Xu, D.Y.: Impulsive effects on stability of Cohen-Grossberg neural networks with variable delays. Applied Math. And Comput. 160, 1–16 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, J., Ding, W., Yang, F., Liang, L., Hong, Q. (2010). Stability of Impulsive Cohen-Grossberg Neural Networks with Delays. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_71

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13278-0_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13277-3

  • Online ISBN: 978-3-642-13278-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics