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Global stability of stochastic high-order neural networks with discrete and distributed delays

Author

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  • Wang, Zidong
  • Fang, Jian’an
  • Liu, Xiaohui

Abstract

High-order neural networks can be considered as an expansion of Hopfield neural networks, and have stronger approximation property, faster convergence rate, greater storage capacity, and higher fault tolerance than lower-order neural networks. In this paper, the global asymptotic stability analysis problem is considered for a class of stochastic high-order neural networks with discrete and distributed time-delays. Based on an Lyapunov–Krasovskii functional and the stochastic stability analysis theory, several sufficient conditions are derived, which guarantee the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the stochastic high-order delayed neural networks under consideration are globally asymptotically stable in the mean square if two linear matrix inequalities (LMIs) are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also shown that the main results in this paper cover some recently published works. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria.

Suggested Citation

  • Wang, Zidong & Fang, Jian’an & Liu, Xiaohui, 2008. "Global stability of stochastic high-order neural networks with discrete and distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 36(2), pages 388-396.
    • Handle: RePEc:eee:chsofr:v:36:y:2008:i:2:p:388-396
    DOI: 10.1016/j.chaos.2006.06.063
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    References listed on IDEAS

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    1. Arik, Sabri, 2005. "Global robust stability analysis of neural networks with discrete time delays," Chaos, Solitons & Fractals, Elsevier, vol. 26(5), pages 1407-1414.
    2. Wang, Zidong & Shu, Huisheng & Liu, Yurong & Ho, Daniel W.C. & Liu, Xiaohui, 2006. "Robust stability analysis of generalized neural networks with discrete and distributed time delays," Chaos, Solitons & Fractals, Elsevier, vol. 30(4), pages 886-896.
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    Cited by:

    1. Zhifu Jia & Cunlin Li, 2023. "Almost Sure Exponential Stability of Uncertain Stochastic Hopfield Neural Networks Based on Subadditive Measures," Mathematics, MDPI, vol. 11(14), pages 1-19, July.
    2. Dong, Zeyu & Wang, Xin & Zhang, Xian, 2020. "A nonsingular M-matrix-based global exponential stability analysis of higher-order delayed discrete-time Cohen–Grossberg neural networks," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    3. Lin, Yi-Kuei, 2010. "Reliability evaluation of a revised stochastic flow network with uncertain minimum time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1253-1258.

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