State estimation for neural neutral-type networks with mixed time-varying delays and Markovian jumping parameters

作     者：S.Lakshmanan Ju H.Park H.Y.Jung P.Balasubramaniam 

作者机构：Department of Information and Communication Engineering/Electrical Engineering Yeungnam University214-1 Dae-DongKyongsan 712-749Republic of Korea Department of MathematicsGandhigram Rural Institute-Deemed UniversityGandhigram - 624 302TamilnaduIndia 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2012年第21卷第10期

页      面：29-37页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 080802[工学-电力系统及其自动化] 0808[工学-电气工程] 07[理学] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：Project supported by the 2010 Yeungnam University Research Grant 

主　　题：neural networks state estimation neutral delay Markovian jumping parameters 

摘      要：This paper is concerned with a delay-dependent state estimator for neutral-type neural networks with mixed timevarying delays and Markovian jumping *** addressed neural networks have a finite number of modes,and the modes may jump from one to another according to a Markov *** construction of a suitable Lyapunov-Krasovskii functional,a delay-dependent condition is developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable in a mean *** criterion is formulated in terms of a set of linear matrix inequalities（LMIs）,which can be checked efficiently by use of some standard numerical packages.