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Global asymptotic stability of uncertain stochastic bi-directional associative memory networks with discrete and distributed delays

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  • Shu, Huisheng
  • Wang, Zidong
  • Lü, Zengwei

Abstract

In this paper, the global asymptotic stability analysis problem is investigated for a class of stochastic bi-directional associative memory (BAM) networks with mixed time-delays and parameter uncertainties. The mixed time-delays consist of both the discrete and the distributed delays, the uncertainties are assumed to be norm-bounded, and the neural network are subject to stochastic disturbances described by a Brownian motion. Without assuming the monotonicity and differentiability of activation functions, we employ the Lyapunov–Krasovskii stability theory and some new developed techniques to establish sufficient conditions for the stochastic delayed BAM networks to be globally asymptotically stable in the mean square. These conditions are expressed in terms of the feasibility to a set of linear matrix inequalities (LMIs) that can be easily checked by utilizing the numerically efficient Matlab LMI toolbox. A simple example is exploited to show the usefulness of the derived LMI-based stability conditions.

Suggested Citation

  • Shu, Huisheng & Wang, Zidong & Lü, Zengwei, 2009. "Global asymptotic stability of uncertain stochastic bi-directional associative memory networks with discrete and distributed delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(3), pages 490-505.
    • Handle: RePEc:eee:matcom:v:80:y:2009:i:3:p:490-505
    DOI: 10.1016/j.matcom.2008.07.007
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    References listed on IDEAS

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    1. Huang, He & Feng, Gang, 2007. "Delay-dependent stability for uncertain stochastic neural networks with time-varying delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 93-103.
    2. Wang, Zidong & Lauria, Stanislao & Fang, Jian’an & Liu, Xiaohui, 2007. "Exponential stability of uncertain stochastic neural networks with mixed time-delays," Chaos, Solitons & Fractals, Elsevier, vol. 32(1), pages 62-72.
    3. Hu, Jin & Zhong, Shouming & Liang, Li, 2006. "Exponential stability analysis of stochastic delayed cellular neural network," Chaos, Solitons & Fractals, Elsevier, vol. 27(4), pages 1006-1010.
    4. Liu, Yurong & Wang, Zidong & Liu, Xiaohui, 2006. "Global asymptotic stability of generalized bi-directional associative memory networks with discrete and distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 28(3), pages 793-803.
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    Cited by:

    1. Wang, Huiwei & Song, Qiankun & Duan, Chengjun, 2010. "LMI criteria on exponential stability of BAM neural networks with both time-varying delays and general activation functions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 837-850.

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