Mean square exponential input-to-state stability of stochastic memristive complex-valued neural networks with time varying delay

被引：30
作者：
Liu, Dan [1 ]
Zhu, Song [1 ]
Chang, Wenting [1 ]
机构：
[1] China Univ Min & Technol, Sch Math, Xuzhou, Jiangsu, Peoples R China
来源：
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE | 2017年 / 48卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Stability; neural networks; stochastic systems; time-varying systems; nonlinear systems; MIXED DELAYS; FINITE-TIME; DISCRETE;
D O I：
10.1080/00207721.2017.1300706
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, mean square exponential input-to-state stability (exp-ISS) of stochastic memristive complex-valued neural networks (SMCVNNs) is investigated. By utilising Lyapunov functional and stochastic analysis theory, a sufficient criterion is derived to assure the mean square exp-ISS of the SMCVNNs. The obtained results not only generalise the previous works in the literature about real-valued neural networks as special cases, but also can be easily checked by parameters of system. Numerical simulations are given to show the effectiveness of our theoretical results.
引用
收藏
页码：1966 / 1977
页数：12
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