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基于忆阻的分数阶时滞复值神经网络的全局渐近稳定性

王利敏 宋乾坤 赵振江

王利敏, 宋乾坤, 赵振江. 基于忆阻的分数阶时滞复值神经网络的全局渐近稳定性[J]. 应用数学和力学, 2017, 38(3): 333-346. doi: 10.21656/1000-0887.370221
引用本文: 王利敏, 宋乾坤, 赵振江. 基于忆阻的分数阶时滞复值神经网络的全局渐近稳定性[J]. 应用数学和力学, 2017, 38(3): 333-346. doi: 10.21656/1000-0887.370221
WANG Li-min, SONG Qian-kun, ZHAO Zhen-jiang. Global Asymptotic Stability of Memristor-Based Fractional-Order Complex-Valued Neural Networks With Time Delays[J]. Applied Mathematics and Mechanics, 2017, 38(3): 333-346. doi: 10.21656/1000-0887.370221
Citation: WANG Li-min, SONG Qian-kun, ZHAO Zhen-jiang. Global Asymptotic Stability of Memristor-Based Fractional-Order Complex-Valued Neural Networks With Time Delays[J]. Applied Mathematics and Mechanics, 2017, 38(3): 333-346. doi: 10.21656/1000-0887.370221

基于忆阻的分数阶时滞复值神经网络的全局渐近稳定性

doi: 10.21656/1000-0887.370221
基金项目: 国家自然科学基金(61273021;61473332); 重庆市研究生科研创新项目(CYS16179)
详细信息
    作者简介:

    王利敏(1993—),女,硕士生(E-mail: liminwangm@163.com);宋乾坤(1963—),男,教授,博士(通讯作者. E-mail: qiankunsong@163.com);赵振江(1961—),男,教授,硕士(E-mail: zhaozjcn@163.com).

  • 中图分类号: O175.13

Global Asymptotic Stability of Memristor-Based Fractional-Order Complex-Valued Neural Networks With Time Delays

Funds: The National Natural Science Foundation of China(61273021; 61473332)
  • 摘要: 研究了分数阶复值神经网络的稳定性.针对一类基于忆阻的分数阶时滞复值神经网络,利用Caputo分数阶微分意义上Filippov解的概念, 研究其平衡点的存在性和唯一性.采用了将复值神经网络分离成实部和虚部的研究方法, 将实数域上的比较原理、不动点定理应用到稳定性分析中, 得到了模型平衡点存在性、唯一性和全局渐近稳定性的充分判据.数值仿真实例验证了获得结果的有效性.
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出版历程
  • 收稿日期:  2016-07-19
  • 修回日期:  2016-11-09
  • 刊出日期:  2017-03-15

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