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

被引：23
|
作者：
Meng, Zhendong [1 ]
Xiang, Zhengrong [1 ]
机构：
[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Jiangsu, Peoples R China
来源：
NEURAL COMPUTING & APPLICATIONS | 2017年 / 28卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Memristor; Recurrent neural networks; Asymptotic stability; Exponential stability; Time-varying delays; EXPONENTIAL STABILITY; SYNCHRONIZATION CONTROL; DISTRIBUTED DELAYS; PASSIVITY ANALYSIS; ROBUST STABILITY; DISCRETE; INTERVAL;
D O I：
10.1007/s00521-015-2146-y
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码：1787 / 1799
页数：13
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