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：

英国工程与自然科学研究理事会; 国家高技术研究发展计划(863计划); 中国国家自然科学基金;

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

页码：914 / 924

页数：11

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