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具有不确定性的分数阶时滞复值神经网络无源性

陈宇 周博 宋乾坤

陈宇, 周博, 宋乾坤. 具有不确定性的分数阶时滞复值神经网络无源性[J]. 应用数学和力学, 2021, 42(5): 492-499. doi: 10.21656/1000-0887.410309
引用本文: 陈宇, 周博, 宋乾坤. 具有不确定性的分数阶时滞复值神经网络无源性[J]. 应用数学和力学, 2021, 42(5): 492-499. doi: 10.21656/1000-0887.410309
CHEN Yu, ZHOU Bo, SONG Qiankun. Passivity of Fractional-Order Delayed Complex-Valued Neural Networks With Uncertainties[J]. Applied Mathematics and Mechanics, 2021, 42(5): 492-499. doi: 10.21656/1000-0887.410309
Citation: CHEN Yu, ZHOU Bo, SONG Qiankun. Passivity of Fractional-Order Delayed Complex-Valued Neural Networks With Uncertainties[J]. Applied Mathematics and Mechanics, 2021, 42(5): 492-499. doi: 10.21656/1000-0887.410309

具有不确定性的分数阶时滞复值神经网络无源性

doi: 10.21656/1000-0887.410309
基金项目: 重庆市教委科学技术研究项目(KJZDM202000701);国家自然科学基金(61773004)
详细信息
    作者简介:

    陈宇(1993—),女,硕士(E-mail: chenyucqjt@163.com);周博(1988—),男,副教授,博士(E-mail: zhoubocncq@163.com);宋乾坤(1963—),男,教授,博士(通讯作者. E-mail: qiankunsong@163.com).

  • 中图分类号: O175

Passivity of Fractional-Order Delayed Complex-Valued Neural Networks With Uncertainties

Funds: The National Natural Science Foundation of China(61773004)
  • 摘要: 该文研究了一类具有不确定性和时滞的分数阶复值神经网络无源性问题,未将复值神经网络模型拆分成两个实值系统,而是将复值系统当成一个整体直接进行处理.通过构造恰当的Lyapunov函数,并利用矩阵不等式技巧,建立了网络无源性的线性矩阵不等式判据.给出的数值例子和仿真验证了获得结论的可行性和有效性.
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出版历程
  • 收稿日期:  2020-10-15
  • 修回日期:  2020-10-21
  • 刊出日期:  2021-05-01

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