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Mean-Square Exponential Input-to-State Stability of Stochastic Fuzzy Recurrent Neural Networks with Multiproportional Delays and Distributed Delays

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  • Tianyu Wang
  • Quanxin Zhu
  • Jingwei Cai

Abstract

We are interested in a class of stochastic fuzzy recurrent neural networks with multiproportional delays and distributed delays. By constructing suitable Lyapunov-Krasovskii functionals and applying stochastic analysis theory, It ’s formula and Dynkin’s formula, we derive novel sufficient conditions for mean-square exponential input-to-state stability of the suggested system. Some remarks and discussions are given to show that our results extend and improve some previous results in the literature. Finally, two examples and their simulations are provided to illustrate the effectiveness of the theoretical results.

Suggested Citation

  • Tianyu Wang & Quanxin Zhu & Jingwei Cai, 2018. "Mean-Square Exponential Input-to-State Stability of Stochastic Fuzzy Recurrent Neural Networks with Multiproportional Delays and Distributed Delays," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, October.
    • Handle: RePEc:hin:jnlmpe:6289019
    DOI: 10.1155/2018/6289019
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