Stability Analysis of Delayed Recurrent Neural Networks Based on a Flexible Terminal Inequality

被引：2
|
作者：
Yu, Shuhang [1 ]
Zhang, Huaguang [2 ,3 ]
Yan, Yuqing [1 ]
Tian, Yufeng [4 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China
[2] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Liaoning, Peoples R China
[3] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China
[4] Chongqing Univ, Coll Automat, Chongqing 400044, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2024年 / 71卷 / 01期
关键词：
Time-varying delay; flexible terminal-based reciprocally convex inequality; recurrent neural networks; stability analysis; GLOBAL ASYMPTOTIC STABILITY; TIME-VARYING DELAYS; FUZZY-SYSTEMS;
D O I：
10.1109/TCSII.2023.3298959
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：
The stability issue of recurrent neural networks (RNNs) with time-varying delay is studied in this brief. By adding a set of flexible terminals, a flexible terminal-based reciprocally convex inequality (FTRCI) relying on one adjustable parameter Q is suggested. Different from the existing estimation techniques, FTRCI employs more slack matrices and utilize more delay information. A novel stability criterion based on linear matrix inequalities (LMIs) is developed using FTRCI. The recently developed stability criterion is demonstrated through the use of a numerical example.
引用
收藏
页码：316 / 320
页数：5
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