随机变时滞神经网络的输入状态稳定性  

Input-to-state Stability for Stochastic Neural Networks with Time-Varying Delay

作　　者：张治中[1] 彭世国[1] Zhang Zhi-zhong;Peng Shi-guo(School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

机构地区：[1]广东工业大学自动化学院,广东广州510006

出　　处：《广东工业大学学报》2017年第4期84-88,共5页Journal of Guangdong University of Technology

基　　金：广东省自然科学基金资助项目(S20130013034;2015A030313485)

摘　　要：给出了一种新的稳定性判据:基于线性矩阵不等式(LMI)的输入状态稳定性判据.基于模型变换,通过构造合适的Lyapunov函数,利用随机分析理论、伊藤(It?'s)公式和一些不等式方法,以线性矩阵不等式形式给出了随机变时滞神经网络的随机输入状态稳定的充分条件.然后通过数值算例说明所提出的方法有较小的保守性,表明所提出方法的有效性.A new stability criterion based on the linear matrix inequality(LMI)input-to-state stability criterion is given.Based on model transformation,input-to-state stability of Stochastic neural networks with Time-Varying Delay is given a sufficient condition in form of linear matrix inequality by constructing appropriate Lyapunov Krasovskii functional,stochastic analysis theory and It?'s formula and applying some inequality approach.Then,the proposed method is proven to be less conservative by illustrating with the numerical examples,which shows the effectiveness of the method.

关 键 词：随机神经网络 变时滞 随机输入状态稳定 LYAPUNOV函数 线性矩阵不等式 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]