DELAY-DEPENDENT STATE ESTIMATION FOR DISCRETE MARKOVIAN JUMP NEURAL NETWORKS WITH TIME-VARYING DELAY

被引：8
|
作者：
Wu, Zhengguang [1 ]
Shi, Peng [2 ,3 ]
Su, Hongye [1 ]
Chu, Jian [1 ]
机构：
[1] Zhejiang Univ, Natl Lab Ind Control Technol, Inst Cyber Syst & Control, Hangzhou 310027, Zhejiang, Peoples R China
[2] Univ Glamorgan, Dept Comp & Math Sci, Pontypridd CF37 1DL, M Glam, Wales
[3] Victoria Univ, Sch Sci & Engn, Melbourne, Vic 8001, Australia
来源：
ASIAN JOURNAL OF CONTROL | 2011年 / 13卷 / 06期
基金：
国家高技术研究发展计划(863计划); 中国国家自然科学基金; 英国工程与自然科学研究理事会;
关键词：
Neural networks; time-varying delays; state estimation; Markovian jumping parameters; linear matrix inequality (LMI); STOCHASTIC EXPONENTIAL STABILITY; GLOBAL ASYMPTOTIC STABILITY; H-INFINITY CONTROL; ROBUST STABILIZATION; LINEAR-SYSTEMS; CRITERIA;
D O I：
10.1002/asjc.219
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The state estimation problem is discussed for discrete Markovian jump neural networks with time-varying delays in terms of linear matrix inequality (LMI) approach. The considered transition probabilities are assumed to be time-variant and partially unknown. The aim of the state estimation problem is to design a state estimator to estimate the neuron states and ensure the stochastic stability of the error-state system. A delay-dependent sufficient condition for the existence of the desired state estimator is proposed. An explicit expression of the desired estimator is also given. A numerical example is introduced to show the effectiveness of the given result.
引用
收藏
页码：914 / 924
页数：11
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