Dynamical Analysis of Neural Networks with Time-Varying Delays Using the LMI Approach

被引：0

作者：

Lakshmanan, Shanmugam ^{[1
]}

Lim, C. P. ^{[1
]}

Bhatti, Asim ^{[1
]}

Gao, David ^{[2
]}

Nahavandi, Saeid ^{[1
]}

机构：

[1] Deakin Univ, Ctr Intelligent Syst Res, Geelong Waurn Ponds Campus, Geelong, Vic 3217, Australia

[2] Federat Univ, Sch Appl & Biomed Sci, Ballarat, Vic, Australia

来源：

NEURAL INFORMATION PROCESSING, PT III | 2015年 / 9491卷

关键词：

Neural networks; Interval time-varying delay; Stability; Linear matrix inequality; STABILITY-CRITERIA; DEPENDENT STABILITY;

D O I：

10.1007/978-3-319-26555-1_34

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay andMarkovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions.

引用

页码：297 / 305

页数：9

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