Exponential Stabilization for Delayed Memristive Neural Networks by Comparison Strategy

被引：0

作者：

Wang, Fenglin ^{[1
]}

Zhao, Jiemei ^{[1
]}

Wu, Ning ^{[1
]}

Li, Yaqin ^{[1
]}

机构：

[1] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China

来源：

IEEE ACCESS | 2025年 / 13卷

关键词：

Delays; Delay effects; State feedback; Adaptive control; Synchronization; Stability criteria; Memristors; Lyapunov methods; Biological neural networks; Switches; Delayed memristive neural networks; exponential stabilization; time-varying delay; Filippov solution; comparison strategy; TIME-VARYING DELAYS; INTERMITTENT CONTROL; SYNCHRONIZATION; STABILITY; SYSTEMS; DESIGN;

D O I：

10.1109/ACCESS.2025.3571786

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article is concerned with the rth moment global exponential stabilization of delayed memristive neural networks (DMNNs). By using the comparison strategy, the theories of differential inclusion and inequality techniques, the exponential stabilization of DMNNs is investigated. To achieve this purpose, a state feedback controller and an adaptive controller are designed, respectively. The comparison strategy is a new analyzed method without employing Lyapunov stability theory and relaxes the constraint of time delays. In addition, the obtained results are represented by algebraic criteria, which are convenient for testing. In the end, a numerical simulation is given to show the validity of the derived criteria.

引用

页码：89343 / 89350

页数：8

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