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时滞Cohen-Grossberg神经网络的全局稳定性

龙兰 徐晓惠 张继业

龙兰, 徐晓惠, 张继业. 时滞Cohen-Grossberg神经网络的全局稳定性[J]. 西南交通大学学报, 2008, 21(3): 381-386.
引用本文: 龙兰, 徐晓惠, 张继业. 时滞Cohen-Grossberg神经网络的全局稳定性[J]. 西南交通大学学报, 2008, 21(3): 381-386.
LONG Lan, XU Xiaohui, ZHANG Jiye. Global Stability Analysis of Cohen-Grossberg Neural Networks with Unbounded Time Delays[J]. Journal of Southwest Jiaotong University, 2008, 21(3): 381-386.
Citation: LONG Lan, XU Xiaohui, ZHANG Jiye. Global Stability Analysis of Cohen-Grossberg Neural Networks with Unbounded Time Delays[J]. Journal of Southwest Jiaotong University, 2008, 21(3): 381-386.

时滞Cohen-Grossberg神经网络的全局稳定性

基金项目: 

国家自然科学基金资助项目(1077215250525518)

教育部留学回国人员科研启动基金

详细信息
    作者简介:

    龙兰(1963- ),女,讲师,研究方向为系统稳定性分析与控制

    通讯作者:

    张继业(1965- ),教授,博士,研究方向为系统稳定性分析与控制,电话:028-86466040,E-mail: jyzhang@home.swjtu.edu.cn

Global Stability Analysis of Cohen-Grossberg Neural Networks with Unbounded Time Delays

  • 摘要: 为将神经网络应用于最优化问题的求解,对具有无穷时滞的Cohen-Grossberg神经网络平衡点的存在性、唯一性和全局渐近稳定性进行了探讨.在不假设激活函数有界性、单调性和可微性的情况下,得到了系统平衡点的存在性条件.利用向量Liapunov函数法,构造适当的含有无穷时滞的微分-积分不等式,并分析了微分-积分不等式的稳定性,得到了Cohen-Grossberg神经网络系统全局渐近稳定性的判据.通过判断由神经网络的权系数、自反馈函数以及激励函数构造的矩阵是否为M-矩阵,即可得到Cohen-Grossberg神经网络系统的全局渐近稳定性.最后给出了一个算例,以说明该判据的正确性.

     

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出版历程
  • 收稿日期:  2007-07-23
  • 刊出日期:  2008-06-25

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