一类具比例时滞脉冲递归神经网络的全局多项式稳定性  

Global Polynomial Stability of a Class of Impulsive Recurrent Neural Networks with Proportional Delays

作　　者：周立群[1] 宋协慧 ZHOU Li-qun;SONG Xie-hui(School of Mathematics Science,Tianjin Normal University,Xiqing Tianjin 300387)

机构地区：[1]天津师范大学数学科学学院,天津西青区300387

出　　处：《电子科技大学学报》2021年第1期91-100,共10页Journal of University of Electronic Science and Technology of China

基　　金：天津市自然科学基金(18JCYBJC85800);天津市高校中青年骨干创新培养计划(135205GC38)。

摘　　要：该文对一类具比例时滞脉冲递归神经网络给出全局多项式稳定性定义。通过引入可调参数,构造适合的Lyapunov泛函和运用线性矩阵不等式(LMI)的方法,对该系统的全局多项式稳定性进行讨论,得到了保证该系统全局多项式稳定的判定准则,且这些准则以LMI的形式给出的,方便应用Matlab工具箱进行验证。该文还揭示了多项式稳定性与指数稳定性之间的关系,最后通过数值算例验证了所得准则的准确性。The definition of global polynomial stability for a class of impulsive recurrent neural networks(IRNNs)with proportional delays is given.By introducing adjustable parameters,several suitable Lyapunov functionals are constructed and the method of linear matrix inequality(LMI)is used to discuss the global polynomial stability of the system.Several criteria for guaranteeing the global polynomial stability of the system are obtained.And these criteria are given in the form of LMI,which is convenient to use Matlab toolbox for verification.The relationship between polynomial stability and exponential stability is revealed.The criteria are verified by numerical examples.

关 键 词：脉冲效应 LYAPUNOV泛函 比例时滞 多项式稳定性 递归神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O175.13[自动化与计算机技术—控制科学与工程]