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Synchronization of competitive neural networks with different time scales

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  • Lou, Xuyang
  • Cui, Baotong

Abstract

In this paper, the exponential synchronization problem for a class of competitive neural networks is investigated. Moreover, without assuming the active functions to be differentiable and bounded, some exponential synchronization criteria are devised by Lyapunov functionals, linear matrix inequality (LMI) approach, the Leibniz–Newton formula. The conditions are less conservative than existing ones reported in the literature for delayed neural networks. The applicability of the conditions are validated by an illustrative example.

Suggested Citation

  • Lou, Xuyang & Cui, Baotong, 2007. "Synchronization of competitive neural networks with different time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 563-576.
    • Handle: RePEc:eee:phsmap:v:380:y:2007:i:c:p:563-576
    DOI: 10.1016/j.physa.2007.02.088
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    References listed on IDEAS

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    1. Li, Ping & Cao, Jinde & Wang, Zidong, 2007. "Robust impulsive synchronization of coupled delayed neural networks with uncertainties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 261-272.
    2. Cheng, Chao-Jung & Liao, Teh-Lu & Hwang, Chi-Chuan, 2005. "Exponential synchronization of a class of chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 197-206.
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    Cited by:

    1. Hu, Jiming, 2009. "Synchronization conditions for chaotic nonlinear continuous neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2495-2501.

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