Dissipativity analysis of stochastic fuzzy neural networks with randomly occurring uncertainties using delay dividing approach

被引：4

作者：

Senthilraj, Sundaram ^{[1
]}

Raja, Ramachandran ^{[2
]}

Cao, Jinde ^{[3
]}

Fardoun, Habib M. ^{[4
]}

机构：

[1] Velalar Coll Engn & Technol, Dept Math, Erode 638012, India

[2] Alagappa Univ, Ramanujan Ctr Higher Math, Karaikkudi 630004, Tamil Nadu, India

[3] Southeast Univ, Sch Math, Nanjing 211189, Jiangsu, Peoples R China

[4] King Abdulaziz Univ, Dept Informat Syst, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia

来源：

NONLINEAR ANALYSIS-MODELLING AND CONTROL | 2019年 / 24卷 / 04期

关键词：

dissipativity; stochastic fuzzy neural network; time-varying delay; TIME-VARYING DELAY; EXPONENTIAL STABILITY; DYNAMICAL-SYSTEMS; SYNCHRONIZATION; PASSIVITY; OUTPUT;

D O I：

10.15388/NA.2019.4.5

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper focuses on the problem of delay-dependent robust dissipativity analysis for a class of stochastic fuzzy neural networks with time-varying delay. The randomly occurring uncertainties under consideration are assumed to follow certain mutually uncorrelated Bernoulli-distributed white noise sequences. Based on the Ito's differential formula, Lyapunov stability theory, and linear matrix inequalities techniques, several novel sufficient conditions are derived using delay partitioning approach to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties. It is shown, by comparing with existing approaches, that the delay-partitioning projection approach can largely reduce the conservatism of the stability results. Numerical examples are constructed to show the effectiveness of the theoretical results.

引用

页码：561 / 581

页数：21

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