State estimation for complex-valued memristive neural networks with time-varying delays

被引：10
作者：
Guo, Runan [1 ]
Zhang, Ziye [1 ]
Gao, Ming [2 ]
机构：
[1] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao, Peoples R China
[2] Shandong Univ Sci & Technol, Coll Min & Safety Engn, Qingdao, Peoples R China
来源：
ADVANCES IN DIFFERENCE EQUATIONS | 2018年
基金：
中国国家自然科学基金;
关键词：
State estimation; Memristive neural networks; Complex-valued systems; Time-varying delays; STABILITY ANALYSIS; EXPONENTIAL STABILITY; ADAPTIVE DYNAMICS; GLOBAL STABILITY; DISSIPATIVITY; DESIGN; SYSTEM; MODEL; SYNCHRONIZATION;
D O I：
10.1186/s13662-018-1575-1
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper focuses on the state estimation problem for complex-valued memristive neural networks with time-varying delays. By utilizing Lyapunov stability theory and some matrix inequality techniques, based on a novel Lyapunov functional, a sufficient delay-dependent condition which guarantees that the error-state system is global asymptotically stable is firstly derived for the addressed system, and a suitable state estimator is also designed. Finally, an example is given to illustrate the present method.
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页数：15
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