Improved Exponential Stability for Delayed Neural Networks With Large Delay Based on Relaxed Piecewise Lyapunov-Krasovskii Functional

被引：5
|
作者：
Fan, Yu-Long [1 ,2 ]
Xu, Jin-Meng [1 ,2 ]
Zhang, Chuan-Ke [1 ,2 ]
Liu, Yunfan [1 ,2 ]
He, Yong [1 ,2 ]
机构：
[1] China Univ Geosci, Sch Automat, Hubei Key Lab Adv Control & Intelligent Automation, Wuhan 430074, Peoples R China
[2] China Univ Geosci, Engn Res Ctr Intelligent Technol Geoexplorat, Minist Educ, Wuhan 430074, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2023年 / 70卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Exponential stability; delayed neural networks; switching; large delay; SYSTEMS;
D O I：
10.1109/TCSII.2023.3237560
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this brief, the stability of neural networks with switching between small and large time delays is studied by developing an improved exponential stability criterion. Firstly, the delayed neural network (DNN) with small delay (SD) and large delay (LD) is modeled as a switched DNN. Then, based on an augmented piecewise Lyapunov-Krasovskii functional with LD-based terms considering relaxed switching constraints, and Wiritinger-based inequality, a stability criterion with less conservatism is developed. Finally, a numerical example is provided to demonstrate the superiority and effectiveness of the proposed method.
引用
收藏
页码：2510 / 2514
页数：5
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