Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction-Diffusion Terms and Markovian Jumping Parameters

被引：7
|
作者：
Wei, Tengda [1 ,2 ]
Wang, Yangfan [3 ]
Wang, Linshan [4 ]
机构：
[1] Ocean Univ China, Coll Ocean & Atmospher Sci, Qingdao 266100, Peoples R China
[2] Univ Dundee, Dept Math, Dundee DD1 4HN, Scotland
[3] Ocean Univ China, Coll Marine Life Sci, Qingdao 266100, Peoples R China
[4] Ocean Univ China, Sch Math Sci, Qingdao 266100, Peoples R China
来源：
NEURAL PROCESSING LETTERS | 2018年 / 48卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Synchronization; Stochastic delayed neural network; Reaction-diffusion; Markovian jumping parameter; Wiener process; TIME-VARYING DELAYS; DISTRIBUTED DELAYS; DYNAMICAL NETWORKS; STABILITY;
D O I：
10.1007/s11063-017-9756-6
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 investigates robust exponential synchronization for stochastic delayed neural networks with reaction-diffusion terms and Markovian jumping parameters driven by infinite dimensional Wiener processes. The novelty of this paper lives in the use of a new Lyapunov-Krasovskii functional and Poincare inequality to present some criteria for robust exponential synchronization in terms of linear matrix inequalities (LMIs) and matrix measure under Robin boundary conditions. Finally, two numerical examples are provided to illustrate the effectiveness of the easily verifiable synchronization LMIs in MATLAB toolbox.
引用
收藏
页码：979 / 994
页数：16
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