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New delay-dependent exponential stability criteria of BAM neural networks with time delays

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  • Yang, Degang
  • Liao, Xiaofeng
  • Hu, Chunyan
  • Wang, Yong

Abstract

In this paper, the global exponential stability is investigated for the bi-directional associative memory networks with time delays. Several new sufficient conditions are presented to ensure global exponential stability of delayed bi-directional associative memory neural networks based on the Lyapunov functional method as well as linear matrix inequality technique. To the best of our knowledge, few reports about such “linearization” approach to exponential stability analysis for delayed neural network models have been presented in literature. The method, called parameterized first-order model transformation, is used to transform neural networks. The obtained conditions show to be less conservative and restrictive than that reported in the literature. Two numerical simulations are also given to illustrate the efficiency of our result.

Suggested Citation

  • Yang, Degang & Liao, Xiaofeng & Hu, Chunyan & Wang, Yong, 2009. "New delay-dependent exponential stability criteria of BAM neural networks with time delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1679-1697.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:5:p:1679-1697
    DOI: 10.1016/j.matcom.2008.08.014
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    References listed on IDEAS

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    1. Wang, Hui & Liao, Xiaofeng & Li, Chuandong, 2007. "Existence and exponential stability of periodic solution of BAM neural networks with impulse and time-varying delay," Chaos, Solitons & Fractals, Elsevier, vol. 33(3), pages 1028-1039.
    2. Li, Chuandong & Liao, Xiaofeng & Zhang, Rong, 2005. "Delay-dependent exponential stability analysis of bi-directional associative memory neural networks with time delay: an LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 24(4), pages 1119-1134.
    3. Huang, Xia & Cao, Jinde & Huang, De-Shuang, 2005. "LMI-based approach for delay-dependent exponential stability analysis of BAM neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 24(3), pages 885-898.
    4. Li, Yongkun, 2005. "Global exponential stability of BAM neural networks with delays and impulses," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 279-285.
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    Cited by:

    1. Yi, Chenfu & Zhang, Yunong & Guo, Dongsheng, 2013. "A new type of recurrent neural networks for real-time solution of Lyapunov equation with time-varying coefficient matrices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 92(C), pages 40-52.
    2. Long, Shujun & Wang, Xiaohu & Li, Dingshi, 2012. "Attracting and invariant sets of non-autonomous reaction-diffusion neural networks with time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(11), pages 2199-2214.
    3. 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.

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