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Attractor and Boundedness of Switched Stochastic Cohen‐Grossberg Neural Networks

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Listed:
  • Chuangxia Huang
  • Jie Cao
  • Peng Wang

Abstract

We address the problem of stochastic attractor and boundedness of a class of switched Cohen‐Grossberg neural networks (CGNN) with discrete and infinitely distributed delays. With the help of stochastic analysis technology, the Lyapunov‐Krasovskii functional method, linear matrix inequalities technique (LMI), and the average dwell time approach (ADT), some novel sufficient conditions regarding the issues of mean‐square uniformly ultimate boundedness, the existence of a stochastic attractor, and the mean‐square exponential stability for the switched Cohen‐Grossberg neural networks are established. Finally, illustrative examples and their simulations are provided to illustrate the effectiveness of the proposed results.

Suggested Citation

  • Chuangxia Huang & Jie Cao & Peng Wang, 2016. "Attractor and Boundedness of Switched Stochastic Cohen‐Grossberg Neural Networks," Discrete Dynamics in Nature and Society, John Wiley & Sons, vol. 2016(1).
    • Handle: RePEc:wly:jnddns:v:2016:y:2016:i:1:n:4958217
    DOI: 10.1155/2016/4958217
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    References listed on IDEAS

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    1. Liu, Linna & Zhu, Quanxin, 2015. "Almost sure exponential stability of numerical solutions to stochastic delay Hopfield neural networks," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 698-712.
    2. Chuangxia Huang & Hanfeng Kuang & Xiaohong Chen & Fenghua Wen, 2013. "An LMI Approach for Dynamics of Switched Cellular Neural Networks with Mixed Delays," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-8, April.
    3. Chuangxia Huang & Hanfeng Kuang & Xiaohong Chen & Fenghua Wen, 2013. "An LMI Approach for Dynamics of Switched Cellular Neural Networks with Mixed Delays," Abstract and Applied Analysis, John Wiley & Sons, vol. 2013(1).
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