Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects

被引：1
|
作者：
Huang, Tingwen [1 ]
Li, Chuandong [2 ]
Duan, Shukai [3 ]
Starzyk, Janusz A. [4 ,5 ]
机构：
[1] Texas A&M Univ Qatar, Doha 23874, Qatar
[2] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China
[3] Southwest Univ, Sch Elect & Informat Engn, Chongqing 400715, Peoples R China
[4] Ohio Univ, Sch Elect Engn & Comp Sci, Athens, OH 45701 USA
[5] Univ Informat Technol & Management, PL-35064 Rzeszow, Poland
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2012年 / 23卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Delayed neural networks (DNN); exponential stability; impulse; mean-square stability; parameter uncertainty; stochastic perturbation; TIME-VARYING DELAYS; GLOBAL ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; LINEAR-SYSTEMS; MIXED DELAYS; DISCRETE; STABILIZATION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained. Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses.
引用
收藏
页码：866 / 875
页数：10
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