Almost Sure Stability of Stochastic Neural Networks with Time Delays in the Leakage Terms

被引：2
|
作者：
Song, Mingzhu [1 ]
Zhu, Quanxin [2 ,3 ,4 ]
Zhou, Hongwei [5 ]
机构：
[1] Tongling Univ, Dept Math & Comp Sci, Tongling 244000, Peoples R China
[2] Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China
[3] Nanjing Normal Univ, Inst Finance & Stat, Nanjing 210023, Jiangsu, Peoples R China
[4] Univ Bielefeld, Dept Math, D-33615 Bielefeld, Germany
[5] Nanjing Xiaozhuang Univ, Sch Math & Informat Technol, Nanjing 211171, Jiangsu, Peoples R China
来源：
DISCRETE DYNAMICS IN NATURE AND SOCIETY | 2016年 / 2016卷
基金：
中国国家自然科学基金;
关键词：
GLOBAL EXPONENTIAL STABILITY; VARYING DELAYS; BAM; EXPECTATIONS; COEFFICIENTS;
D O I：
10.1155/2016/2487957
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The stability issue is investigated for a class of stochastic neural networks with time delays in the leakage terms. Different from the previous literature, we are concerned with the almost sure stability. By using the LaSalle invariant principle of stochastic delay differential equations, Ito's formula, and stochastic analysis theory, some novel sufficient conditions are derived to guarantee the almost sure stability of the equilibrium point. In particular, the weak infinitesimal operator of Lyapunov functions in this paper is not required to be negative, which is necessary in the study of the traditional moment stability. Finally, two numerical examples and their simulations are provided to show the effectiveness of the theoretical results and demonstrate that time delays in the leakage terms do contribute to the stability of stochastic neural networks.
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收藏
页数：10
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