Advertisement

Global Asymptotical Stability in Neutral-Type Delayed Neural Networks with Reaction-Diffusion Terms

  • Jianlong Qiu
  • Jinde Cao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

In this paper, the global uniform asymptotical stability is studied for delayed neutral-type neural networks by constructing appropriate Lyapunov functional and using the linear matrix inequality (LMI) approach. The main condition given in this paper is dependent on the size of the measure of the space, which is usually less conservative than space-independent ones. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed criteria.

Keywords

Neural Network Linear Matrix Inequality Exponential Stability Recurrent Neural Network Cellular Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cao, J., Zhou, D.: Stability Analysis of Delayed Cellular Neural Networks. Neural Networks 11, 1601–1605 (1998)CrossRefGoogle Scholar
  2. 2.
    Cao, J., Wang, J.: Global Exponential Stability and Periodicity of Recurrent Neural Networks with Time Delays. IEEE Trans. Circuits Syst. I 52(5), 925–931 (2005)MathSciNetGoogle Scholar
  3. 3.
    Cao, J., Ho, D.W.C.: A General Framework for Global Asymptotic Stability Analysis of Delayed Neural Networks Based on LMI Approach. Chaos, Solitons and Fractals 24(5), 1317–1329 (2005)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Gu, K.: An Integral Inequality in the Stability Problems of Time-delay Systems. In: Proceedings of 39th IEEE CDC, Sydney, Australia, pp. 2805–2810 (2000)Google Scholar
  5. 5.
    Liao, X.X., Fu, Y., Gao, J., Zhao, X.: Stability of Hopfield Neural Networks with Reaction-diffusion Terms. Acta Electron. Sinica 28, 78–80 (2002)Google Scholar
  6. 6.
    Liang, J., Cao, J.: Global Exponential Stability of Reaction-diffusion Recurrent Neural Networks with Time-varying Delays. Phys. Lett. A 314, 434–442 (2003)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Wang, L., Xu, D.: Global Exponential Stability of Reaction-diffusion Hopfield Neural Networks with Time-varying Delays. Science in China E 33, 488–495 (2003)Google Scholar
  8. 8.
    Xu, S., Lam, J., Ho, D.W.C., Zou, Y.: Delay-dependent Exponential Stability for a Class of Neural Networks with Time Delays. J. Comput. Appl. Math. 183, 16–28 (2005)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Vidyasagar, M.: Nonliear Systems Analysis. Englewood Cliffs, New Jersey (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jianlong Qiu
    • 1
    • 2
  • Jinde Cao
    • 1
  1. 1.Department of MathematicsSoutheast UniversityNanjingChina
  2. 2.Department of MathematicsLinyi Normal UniversityLinyiChina

Personalised recommendations