Global exponential stability of stochastic memristor-based complex-valued neural networks with time delays

被引：22
|
作者：
Liu, Dan [1 ]
Zhu, Song [1 ]
Chang, Wenting [1 ]
机构：
[1] China Univ Min & Technol, Sch Math, Xuzhou 221116, Peoples R China
来源：
NONLINEAR DYNAMICS | 2017年 / 90卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Stochastic; Complex-valued neural networks; Memristor; Global exponential stability; Time delays; TO-STATE STABILITY; PASSIVITY ANALYSIS; DISSIPATIVITY; SYNCHRONIZATION; SYNAPSES;
D O I：
10.1007/s11071-017-3702-z
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
In recent years, the dynamic behaviors of complex-valued neural networks have been extensively investigated in a variety of areas. This paper focuses on the stability of stochastic memristor-based complex-valued neural networks with time delays. By using the Lyapunov stability theory, Halanay inequality and It formula, new sufficient conditions are obtained for ensuring the global exponential stability of the considered system. Moreover, the obtained results not only generalize the previously published corresponding results as special cases for our results, but also can be checked with the parameters of system itself. Finally, simulation results in three numerical examples are discussed to illustrate the theoretical results.
引用
收藏
页码：915 / 934
页数：20
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