A new approach to stability analysis of neural networks with time-varying delay via novel Lyapunov-Krasovskii functional

查看全文 作　　者：[1]S.M.Lee;[2]O.M.Kwon;Ju [3]H.Park 机构地区：[1]Department of Electronic Engineering,Daegu University,Gyungsan,Gyungbuk 712-714,Republic of Korea;[2]College of Electrical and Computer Engineering,410 SungBong-Ro,Heungduk-Gu,Chungbuk National University,Cheongju 361-763,Republic of Korea;[3]Nonlinear Dynamics Group,Department of Electrical Engineering,Yeungnam University,214-1 Dae-Dong,Kyongsan 712-749,Republic of Korea 出　　处：《Chinese Physics B》索引2010年第19卷第5期,共6页 基　　金：Project supported by the Daegu University Research Grant,2009 摘　　要：In this paper,new delay-dependent stability criteria for asymptotic stability of neural networks with time-varying delays are derived.The stability conditions are represented in terms of linear matrix inequalities(LMIs) by constructing new Lyapunov-Krasovskii functional.The proposed functional has an augmented quadratic form with states as well as the nonlinear function to consider the sector and the slope constraints.The less conservativeness of the proposed stability criteria can be guaranteed by using convex properties of the nonlinear function which satisfies the sector and slope bound.Numerical examples are presented to show the effectiveness of the proposed method. 关 键 词：LYAPUNOV 稳定标准 神经网络分析 时变 泛函 非线性函数 延迟 时滞神经网络