Stability analysis of stochastic memristor-based recurrent neural networks with mixed time-varying delays

被引：0
|
作者：
Zhendong Meng
Zhengrong Xiang
机构：
[1] Nanjing University of Science and Technology,School of Automation
来源：
Neural Computing and Applications | 2017年 / 28卷
关键词：
Memristor; Recurrent neural networks; Asymptotic stability; Exponential stability; Time-varying delays;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, the stability problem of stochastic memristor-based recurrent neural networks with mixed time-varying delays is investigated. Sufficient conditions are established in terms of linear matrix inequalities which can guarantee that the stochastic memristor-based recurrent neural networks are asymptotically stable and exponentially stable in the mean square, respectively. Two examples are given to demonstrate the effectiveness of the obtained results.
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页码：1787 / 1799
页数：12
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