Delay-dependent stability criteria of uncertain Markovian jump neural networks with discrete interval and distributed time-varying delays

被引：82
作者：
Ali, M. Syed [1 ]
Arik, Sabri [2 ]
Saravanakurnar, R. [1 ]
机构：
[1] Thiruvalluvar Univ, Dept Math, Vellore 632115, Tamil Nadu, India
[2] Istanbul Univ, Dept Comp Engn, TR-34320 Istanbul, Turkey
来源：
NEUROCOMPUTING | 2015年 / 158卷
关键词：
Distributed time-varying delay; Interval time-varying delay; Linear matrix inequality (LMI); Markovian jumping parameters; Neural networks; ROBUST STABILITY; STOCHASTIC STABILITY; STATE ESTIMATION; SYSTEMS;
D O I：
10.1016/j.neucom.2015.01.056
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a class of uncertain neural networks with discrete interval and distributed time-varying delays and Markovian jumping parameters (MJPs) are carried out. The Markovian jumping parameters are modeled as a continuous-time, finite-state Markov chain. By using the Lyapunov-Krasovskii functionals (LKFs) and linear matrix inequality technique, some new delay-dependent criteria is derived to guarantee the mean-square asymptotic stability of the equilibrium point. Numerical simulations are given to demonstrate the effectiveness of the proposed method. The results are also compared with the existing results to show the less conservativeness. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：167 / 173
页数：7
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