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Existence and Global Exponential Stability of Equilibrium Solution to Reaction-Diffusion Recurrent Neural Networks on Time Scales

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  • Kaihong Zhao
  • Yongkun Li

Abstract

The existence of equilibrium solutions to reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales is proved by the topological degree theory and M-matrix method. Under some sufficient conditions, we obtain the uniqueness and global exponential stability of equilibrium solution to reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales by constructing suitable Lyapunov functional and inequality skills. One example is given to illustrate the effectiveness of our results.

Suggested Citation

  • Kaihong Zhao & Yongkun Li, 2010. "Existence and Global Exponential Stability of Equilibrium Solution to Reaction-Diffusion Recurrent Neural Networks on Time Scales," Discrete Dynamics in Nature and Society, Hindawi, vol. 2010, pages 1-12, August.
    • Handle: RePEc:hin:jnddns:624619
    DOI: 10.1155/2010/624619
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