Finite-Time Stability of Stochastic Cohen-Grossberg Neural Networks with Markovian Jumping Parameters and Distributed Time-Varying Delays

被引：10

作者：

Arslan, Emel ^{[1
]}

Ali, M. Syed ^{[2
]}

Saravanan, S. ^{[2
]}

机构：

[1] Istanbul Univ, Dept Comp Engn, Fac Engn, TR-34320 Istanbul, Turkey

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

来源：

NEURAL PROCESSING LETTERS | 2017年 / 46卷 / 01期

关键词：

Finite-time stability; Cohen-Grossberg neural networks; Lyapunov-Krasovskii method; Stochastic; Time-varying delay; EXPONENTIAL STABILITY; STATE ESTIMATION; BOUNDEDNESS; STABILIZATION; SYSTEMS;

D O I：

10.1007/s11063-016-9574-2

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, the finite-time stability problem is considered for a class of stochastic Cohen-Grossberg neural networks (CGNNs) with Markovian jumping parameters and distributed time-varying delays. Based on Lyapunov-Krasovskii functional and stability analysis theory, a linear matrix inequality approach is developed to derive sufficient conditions for guaranteeing the stability of the concerned system. It is shown that the addressed stochastic CGNNs with Markovian jumping and distributed time varying delays are finite-time stable. An illustrative example is provided to show the effectiveness of the developed results.

引用

页码：71 / 81

页数：11

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