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Exponential p-convergence analysis for stochastic BAM neural networks with time-varying and infinite distributed delays

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  • Li, Liangliang
  • Jian, Jigui

Abstract

This paper is concerned with the problem of global exponential p-convergence for stochastic BAM neural networks with time-varying and infinite distributed delays. By constructing a new delay differential-integral inequality and a novel L-operator differential-integral inequality, and coupling with stochastic analysis techniques, some delay-dependent sufficient conditions are derived to guarantee exponential p-convergence and the state variables of the discussed stochastic BAM neural networks are globally exponentially convergent to a ball in the state space with a pre-specified convergence rate. Meanwhile, the exponential p-convergent balls are also estimated. Here, the existence and the uniqueness of the equilibrium point needs not to be considered. Finally, two examples with numerical simulations are given to illustrate the effectiveness of the theoretical results.

Suggested Citation

  • Li, Liangliang & Jian, Jigui, 2015. "Exponential p-convergence analysis for stochastic BAM neural networks with time-varying and infinite distributed delays," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 860-873.
    • Handle: RePEc:eee:apmaco:v:266:y:2015:i:c:p:860-873
    DOI: 10.1016/j.amc.2015.06.022
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    References listed on IDEAS

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    1. Xiong, Wenjun & Ma, Deyi & Liang, Jinling, 2009. "Robust convergence of Cohen–Grossberg neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 40(3), pages 1176-1184.
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    6. Sheng, Li & Yang, Huizhong, 2009. "Robust stability of uncertain Markovian jumping Cohen–Grossberg neural networks with mixed time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2120-2128.
    7. Zhao, Hongyong & Ding, Nan, 2007. "Dynamic analysis of stochastic bidirectional associative memory neural networks with delays," Chaos, Solitons & Fractals, Elsevier, vol. 32(5), pages 1692-1702.
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    Cited by:

    1. Zhao, Yongshun & Li, Xiaodi & Cao, Jinde, 2020. "Global exponential stability for impulsive systems with infinite distributed delay based on flexible impulse frequency," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    2. Xiaohui Xu & Jibin Yang & Yanhai Xu, 2019. "Mean Square Exponential Stability of Stochastic Complex-Valued Neural Networks with Mixed Delays," Complexity, Hindawi, vol. 2019, pages 1-20, June.
    3. Xiaohui Xu & Jiye Zhang & Quan Xu & Zilong Chen & Weifan Zheng, 2017. "Impulsive Disturbances on the Dynamical Behavior of Complex-Valued Cohen-Grossberg Neural Networks with Both Time-Varying Delays and Continuously Distributed Delays," Complexity, Hindawi, vol. 2017, pages 1-12, October.
    4. Nagamani, G. & Ramasamy, S., 2016. "Stochastic dissipativity and passivity analysis for discrete-time neural networks with probabilistic time-varying delays in the leakage term," Applied Mathematics and Computation, Elsevier, vol. 289(C), pages 237-257.
    5. Zheng, Bibo & Wang, Zhanshan, 2022. "Mittag-Leffler synchronization of fractional-order coupled neural networks with mixed delays," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    6. Jian, Jigui & Wang, Baoxian, 2015. "Global Lagrange stability for neutral-type Cohen–Grossberg BAM neural networks with mixed time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 116(C), pages 1-25.
    7. Jian, Jigui & Wu, Kai & Wang, Baoxian, 2020. "Global Mittag-Leffler boundedness and synchronization for fractional-order chaotic systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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