一类分数阶复值神经网络的有限时间同步  

Finite-time synchronization for a class of fractional-order complex-valued neural networks

作　　者：朱佳庆 姚宇 张国东[1] ZHU Jiaqing;YAO Yu;ZHANG Guodong(School of Mathematics and Statistics,South-Central Minzu University,Wuhan 430074,China)

机构地区：[1]中南民族大学数学与统计学学院,湖北武汉430074

出　　处：《南通大学学报（自然科学版）》2023年第4期62-68,共7页Journal of Nantong University(Natural Science Edition)　

基　　金：国家自然科学基金面上项目(61976228);湖北省自然科学基金面上项目(2019CFB618)。

摘　　要：通过建立新的杂合控制协议,研究了带状态依赖切换项的分数阶复值神经网络的有限时间同步问题。通过恰当的Lyapunov函数及不等式放缩技巧,给出了此类分数阶复值神经网络的有限时间同步判据。所给出的同步判据是建立在非分离法的基础上,无需将文中的复值系统分离成两个实值系统来讨论,所得结果更符合复值网络本身的意义。最后,通过数值仿真验证了本文所得同步结果的有效性。所构建的杂合控制协议可应用于带状态切换的其他网络模型,能有效解决网络系统跳变不确定性给同步研究带来的难题。In this study,finite-time synchronization of fractional-order complex-valued neural networks with state-dependent switching items is investigated through the establishment of a novel hybrid control protocol.The developed hybrid control protocol can be applied to other network models with state switching,effectively addressing the challenges posed by the transitional uncertainties in network systems.Sufficient criteria for finite-time synchronization of such fractional-order complex-valued neural networks are provided using an appropriate Lyapunov function and inequality scaling techniques.These synchronization criteria are based on a non-separation method,eliminating the need to divide the complex-valued system into two real-valued systems for analysis,thus more accurately reflecting the essence of complex-valued networks.The effectiveness of the obtained synchronization results is validated through numerical simulations.

关 键 词：复值神经网络 有限时间同步 分数阶 状态依赖切换 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]