State estimation for discrete Markovian jumping neural networks with time delay

被引：83
|
作者：
Wu, Zhengguang [1 ]
Su, Hongye [1 ]
Chu, Jian [1 ]
机构：
[1] Zhejiang Univ, Inst Cyber Syst & Control, Natl Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China
来源：
NEUROCOMPUTING | 2010年 / 73卷 / 10-12期
基金：
国家高技术研究发展计划(863计划);
关键词：
Neural networks; Time-varying delays; State estimation; Markovian jumping parameters; Linear matrix inequality (LMI); GLOBAL ASYMPTOTIC STABILITY; STOCHASTIC EXPONENTIAL STABILITY; COMPLEX NETWORKS; VARYING DELAYS; LINEAR-SYSTEMS; CRITERIA; SYNCHRONIZATION;
D O I：
10.1016/j.neucom.2010.01.010
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The state estimation problem for discrete neural networks with Markovian jumping parameters and time-varying delays is investigated. The considered transition probabilities of the mode jumps are assumed to be partially unknown. The purpose of the state estimation problem is to design a state estimator to estimate the neuron states ensuring the dynamics of the estimation error stochastically stable. In terms of a novel Lyapunov functional, the delay-dependent sufficient conditions for the existence of desired state estimator are derived. A numerical example is given to show the validness of the established results. (C) Elsevier B.V. All rights reserved.
引用
收藏
页码：2247 / 2254
页数：8
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