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Exponential Stability and Periodicity of Fuzzy Delayed Reaction-Diffusion Cellular Neural Networks with Impulsive Effect

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  • Guowei Yang
  • Yonggui Kao
  • Changhong Wang

Abstract

This paper considers dynamical behaviors of a class of fuzzy impulsive reaction-diffusion delayed cellular neural networks (FIRDDCNNs) with time-varying periodic self-inhibitions, interconnection weights, and inputs. By using delay differential inequality, -matrix theory, and analytic methods, some new sufficient conditions ensuring global exponential stability of the periodic FIRDDCNN model with Neumann boundary conditions are established, and the exponential convergence rate index is estimated. The differentiability of the time-varying delays is not needed. An example is presented to demonstrate the efficiency and effectiveness of the obtained results.

Suggested Citation

  • Guowei Yang & Yonggui Kao & Changhong Wang, 2013. "Exponential Stability and Periodicity of Fuzzy Delayed Reaction-Diffusion Cellular Neural Networks with Impulsive Effect," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-9, March.
    • Handle: RePEc:hin:jnlaaa:645262
    DOI: 10.1155/2013/645262
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