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

被引：1
|
作者：
Tengda Wei
Yangfan Wang
Linshan Wang
机构：
[1] Ocean University of China,College of Oceanic and Atmospheric Sciences
[2] University of Dundee,Department of Mathematics
[3] Ocean University of China,College of Marine Life Science
[4] Ocean University of China,School of Mathematical Sciences
来源：
Neural Processing Letters | 2018年 / 48卷
关键词：
Synchronization; Stochastic delayed neural network; Reaction–diffusion; Markovian jumping parameter; Wiener process; 34D06; 90B15; 60J10; 34K50;
D O I：
暂无
中图分类号：
学科分类号：
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 Poincaré 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.
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收藏
页码：979 / 994
页数：15
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