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Exponential stability of stochastic cellular neural networks with mixed delays

Author

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  • Xiaofei Li
  • Deng Ding
  • Di Sang

Abstract

In this paper, almost sure exponential stability and pth moment exponential stability of stochastic cellular neural networks with mixed delays are investigated. Employing the methods of stochastic analysis, the Lyapunov’s method, and useful inequality techniques, a sufficient condition ensuring the almost sure exponential stability and pth moment exponential stability is obtained. Two examples are given to illustrate this sufficient condition.

Suggested Citation

  • Xiaofei Li & Deng Ding & Di Sang, 2018. "Exponential stability of stochastic cellular neural networks with mixed delays," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(19), pages 4881-4894, October.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:19:p:4881-4894
    DOI: 10.1080/03610926.2018.1459710
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    Cited by:

    1. Usa Humphries & Grienggrai Rajchakit & Pramet Kaewmesri & Pharunyou Chanthorn & Ramalingam Sriraman & Rajendran Samidurai & Chee Peng Lim, 2020. "Stochastic Memristive Quaternion-Valued Neural Networks with Time Delays: An Analysis on Mean Square Exponential Input-to-State Stability," Mathematics, MDPI, vol. 8(5), pages 1-26, May.

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