Finite-time state estimation for delayed Hopfield neural networks with Markovian jump

被引：23
|
作者：
Wang, Tianbo [1 ]
Zhao, Shouwei [1 ]
Zhou, Wuneng [2 ]
Yu, Weiqin [1 ]
机构：
[1] Shanghai Univ Engn Sci, Coll Fundamental Studies, Shanghai 201620, Peoples R China
[2] Donghua Univ, Coll Informat Sci & Technol, Shanghai 200051, Peoples R China
来源：
NEUROCOMPUTING | 2015年 / 156卷
基金：
中国国家自然科学基金;
关键词：
Finite-time state estimation; Hopfield neural network; Time delay; Markov jump; DEPENDENT EXPONENTIAL STABILITY; VARYING DELAY; UNCERTAIN PARAMETERS; SYSTEMS; SYNCHRONIZATION; STABILIZATION;
D O I：
10.1016/j.neucom.2014.12.062
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the finite-time state estimation problem of delayed Hopfield neural networks with Markovian jump is investigated. The activation functions are assumed to satisfy the section condition. A discontinuous estimator is designed through available output measurements such that the estimation error converges to the origin in finite time. The conditions that the desired estimator parameters need to satisfy are derived by using the Lyapunov stability theory and inequality technique. These conditions are provided in terms of the linear matrix inequalities. Finally, the effectiveness of the proposed method is illustrated by means of a numerical example. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：193 / 198
页数：6
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