Augmented Lyapunov approach to H_∞ state estimation of static neural networks with discrete and distributed time-varying delays  

作　　者：M.Syed Ali R.Saravanakumar 

机构地区：[1]Department of Mathematics, Thiruvalluvar University

出　　处：《Chinese Physics B》2015年第5期140-147,共8页中国物理B（英文版）

基　　金：supported by the Fund from National Board of Higher Mathematics(NBHM),New Delhi(Grant No.2/48/10/2011-R&D-II/865)

摘　　要：This paper deals with H∞ state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H∞ state estimation is proposed to estimate the H∞ performance and global asymptotic stability of the concerned neural networks. By constructing the Lyapunov-Krasovskii functional and using the linear matrix inequality technique, sufficient conditions for delay-dependent H∞ performances are obtained, which can be easily solved by some standard numerical algorithms. Finally, numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.This paper deals with H∞ state estimation problem of neural networks with discrete and distributed time-varying delays. A novel delay-dependent concept of H∞ state estimation is proposed to estimate the H∞ performance and global asymptotic stability of the concerned neural networks. By constructing the Lyapunov-Krasovskii functional and using the linear matrix inequality technique, sufficient conditions for delay-dependent H∞ performances are obtained, which can be easily solved by some standard numerical algorithms. Finally, numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.

关 键 词：distributed delay H∞ state estimation neural networks stability analysis 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O212.1[自动化与计算机技术—控制科学与工程]