Stability Analysis of Delayed Neural Networks via Composite-Matrix-Based Integral Inequality

被引：2
|
作者：
Shi, Yupeng [1 ]
Ye, Dayong [2 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
[2] Northeastern Univ, Sch Mech Engn & Automation, Shenyang 110819, Peoples R China
来源：
MATHEMATICS | 2023年 / 11卷 / 11期
关键词：
neural networks; time-varying delay; stability analysis; integral inequality; delay derivative; TIME-VARYING DELAYS; DEPENDENT STABILITY; SYSTEMS;
D O I：
10.3390/math11112518
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This paper revisits the problem of stability analyses for neural networks with time-varying delay. A composite-matrix-based integral inequality (CMBII) is presented, which takes the delay derivative into account. In this case, the coupling information can be fully captured in integral inequalities with the delay derivative. Based on a CMBII, a new stability criterion is derived for neural networks with time-varying delay. The effectiveness of this method is verified by a numerical example.
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页数：13
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