摘要
研究了一类含有时变时滞的静态神经网络的L_2-L_∞状态估计问题.通过利用增广的Lyapunov-Krasovskii泛函、Wirtinger积分不等式和新的L_2-L_∞性能分析方法,得到了线性矩阵不等式表示的新的L_2-L_∞状态估计器设计充分条件.所设计的状态估计器不仅能保证误差系统的全局指数稳定性,而且满足规定的L_2-L_∞性能要求.最后,数值例子验证了文中所提出设计方法的优越性.
This paper investigates the state estimation problem for a class of static neural networks with time-varying delay.By using the augmented Lyapunov-Krasovskii functionals,the Wirtinger integral inequality and the new L2-L∞performance analysis approach,new sufficient conditions of designing L2-L∞state estimator are obtained,which are expressed by linear matrix inequalities.The designed state estimators of this paper not only guarantee the globally exponential stability of the error system,but also satisfy the prescribed L2-L∞performance requirement.Finally,the numerical example verifies the superiority of the proposed design approach in this paper.
作者
陈玉珍
张涵
张亮亮
陈永刚
Chen Yuzhen;Zhang Han;Zhang Liangliang;Chen Yonggang(School of Mathematical Sciences,Henan Institute of Science and Technology,Xinxiang 453003,China;International College,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《河南师范大学学报（自然科学版）》
CAS
北大核心
2018年第5期118-124,共7页
Journal of Henan Normal University(Natural Science Edition)
基金
国家自然科学基金(61773156)
关键词
静态神经网络
时变时滞
状态估计器
指数稳定性
L2-L∞性能
static neural networks
time-varying delay
state estimator
exponential stability
L 2-L ∞ performance
