Exponential stability of semi-Markovian jump generalized neural networks with interval time-varying delays

被引：38

作者：

Rajchakit, Grienggrai ^{[1
]}

Saravanakumar, R. ^{[1
,2
]}

机构：

[1] Maejo Univ, Dept Math, Fac Sci, Chiang Mai 50290, Thailand

[2] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India

来源：

NEURAL COMPUTING & APPLICATIONS | 2018年 / 29卷 / 02期

关键词：

Exponential stability; Generalized neural networks; Interval time-varying delay; Semi-Markovian jump design; H-INFINITY SYNCHRONIZATION; STOCHASTIC STABILITY; LEAKAGE DELAYS; DISCRETE; CRITERIA; SYSTEMS;

D O I：

10.1007/s00521-016-2461-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This draft addresses the exponential stability problem for semi-Markovian jump generalized neural networks (S-MJGNNs) with interval time-varying delays. The exponential stability conditions are derived by establishing a suitable Lyapunov-Krasovskii functional and applying new analysis method. Improved results are obtained to guarantee the exponential stability of S-MJGNNs through improved reciprocally convex combination and new weighted integral inequality techniques. The method in this paper shows the advantages over some existing ones. To verify the advantages and benefits of employing proposed method is explained through numerical examples.

引用

页码：483 / 492

页数：10

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