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Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks

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  • 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, Hindawi, vol. 2016, pages 1-19, May.
    • Handle: RePEc:hin:jnddns:4958217
    DOI: 10.1155/2016/4958217
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    Cited by:

    1. Sudesh Kumari & Renu Chugh & Jinde Cao & Chuangxia Huang, 2019. "Multi Fractals of Generalized Multivalued Iterated Function Systems in b -Metric Spaces with Applications," Mathematics, MDPI, vol. 7(10), pages 1-17, October.
    2. Sun, Li & Zhu, Haitao & Ding, Yanhui, 2020. "Impulsive control for persistence and periodicity of logistic systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 294-305.

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