Delay decomposition approach to state estimation of neural networks with mixed time-varying delays and Markovian jumping parameters

被引：17

作者：

Lakshmanan, S. ^{[1
,2
]}

Vembarasan, V. ^{[2
]}

Balasubramaniam, P. ^{[2
]}

机构：

[1] Yeungnam Univ, Dept Informat & Commun Engn, Kyongsan 712749, South Korea

[2] Deemed Univ, Gandhigram Rural Inst, Dept Math, Gandhigram 624302, Tamil Nadu, India

来源：

MATHEMATICAL METHODS IN THE APPLIED SCIENCES | 2013年 / 36卷 / 04期

关键词：

linear matrix inequality; LyapunovKrasovskii functional; state estimation; neural networks; Markovian jumping parameters; ROBUST EXPONENTIAL STABILITY; DISTRIBUTED DELAYS; LMI APPROACH; DESIGN; DISCRETE; CRITERIA; SYSTEMS;

D O I：

10.1002/mma.2597

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper investigates the state estimation of neural networks with mixed time-varying delays and Markovian jumping parameters. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. On the basis of the new LyapunovKrasovskii functional, some inequality techniques, stochastic stability theory and delay-dependent stability criteria are obtained in terms of linear matrix inequalities. Finally, three numerical examples are given to illustrate the less conservative and effectiveness of our theoretical results. Copyright (c) 2012 John Wiley & Sons, Ltd.

引用

页码：395 / 412

页数：18

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