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具有时滞的双向联想记忆(BAM)的神经网络的全局动力学行为

周进 刘曾荣 向兰

周进, 刘曾荣, 向兰. 具有时滞的双向联想记忆(BAM)的神经网络的全局动力学行为[J]. 应用数学和力学, 2005, 26(3): 300-308.
引用本文: 周进, 刘曾荣, 向兰. 具有时滞的双向联想记忆(BAM)的神经网络的全局动力学行为[J]. 应用数学和力学, 2005, 26(3): 300-308.
ZHOU Jin, LIU Zeng-rong, XIANG Lan. Global Dynamics of Delayed Bidirectional Associative Memory (BAM) Neural Networks[J]. Applied Mathematics and Mechanics, 2005, 26(3): 300-308.
Citation: ZHOU Jin, LIU Zeng-rong, XIANG Lan. Global Dynamics of Delayed Bidirectional Associative Memory (BAM) Neural Networks[J]. Applied Mathematics and Mechanics, 2005, 26(3): 300-308.

具有时滞的双向联想记忆(BAM)的神经网络的全局动力学行为

基金项目: 国家自然科学基金资助项目(60474071;10171061);中国国家博士后科学基金资助项目(20040350121);河北省教委科研计划资助项目(2003103);河北省高校应用数学与物理重点学科建设资助项目
详细信息
    作者简介:

    周进(1963- ),男,重庆人,教授,博士(联系人.Tel:+86-21-55073261;Fax:+86-22-26543322;E-mail:jinzhou@fudan.edu.cn).

  • 中图分类号: O175;TN911

Global Dynamics of Delayed Bidirectional Associative Memory (BAM) Neural Networks

  • 摘要: 在没有假定关联函数的光滑性,单调性和有界性的条件下,应用Liapunov泛函方法和矩阵代数技术,得到具有常数传输时滞的双向联想记忆(BAM)的神经网络模型平衡点存在性和全局指数稳定性的一些新的充分条件,这些条件可以由网络参数,连接矩阵和关联函数的Lipschitz常数所表示的M矩阵来刻化.这些结果不仅是简单和实用的,而且相对于已有文献的结果具有较少的限制和更易于验证.
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出版历程
  • 收稿日期:  2003-07-04
  • 修回日期:  2004-11-30
  • 刊出日期:  2005-03-15

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