State estimation for proportional delayed complex-valued memristive neural networks

被引：0
|
作者：
Zhang, Yongkang [1 ]
Zhou, Liqun [1 ]
机构：
[1] Tianjin Normal Univ, Sch Math Sci, Tianjin 300387, Peoples R China
来源：
INFORMATION SCIENCES | 2024年 / 680卷
关键词：
Complex-valued memristive neural networks; Proportional delays; State estimation; Linear matrix inequalities; EXPONENTIAL SYNCHRONIZATION; PASSIVITY ANALYSIS; STABILITY; DISSIPATIVITY; DISCRETE;
D O I：
10.1016/j.ins.2024.121150
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The paper addresses the issue of state estimation for a kind of complex-valued memristive neural networks (CVMNNs) accompanied by proportional delays, without applying regular split method of CVMNNs. By virtue of constructing pertinent Lyapunov functional (LF), and deploying matrix inequality techniques, various delay-dependent principles for scrutinizing the asymptotical stability of the estimation error system of the CVMNNs are constituted by linear matrix inequalities (LMIs) with CV variables. Examples are portrayed to manifest the validity and accuracy of the raised principles, and demonstrated applications in image security.
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页数：14
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