Quasi-synchronization of stochastic memristor-based neural networks with mixed delays and parameter mismatches

被引：0
|
作者：
Yinfang Song
Zhigang Zeng
Wen Sun
Feng Jiang
机构：
[1] Yangtze University,School of Information and Mathematics
[2] Huazhong University of Science and Technology,School of Automation
[3] Zhongnan University of Economics and Law,School of Statistics and Mathematics
来源：
Neural Computing and Applications | 2020年 / 32卷
关键词：
Quasi-synchronization; Stochastic memristor-based neural networks; Mixed delays; Parameter mismatches; Lyapunov function;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper is concerned with quasi-synchronization of stochastic memristor-based neural networks with mixed delays and parameter mismatches. Due to the parameter mismatches, mean-square exponential synchronization generally cannot be achieved directly, then the concept of exponential quasi-synchronization in mean square is introduced. Furthermore, based on the differential inclusions theory, stochastic Lyapunov function method and inequality techniques, some sufficient conditions are derived to guarantee the mean-square exponential quasi-synchronization for stochastic memristor-based neural networks with mixed delays. Finally, two examples are given to show the effectiveness of the proposed theoretical results.
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页码：4615 / 4628
页数：13
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