Global Stability of Bidirectional Associative Memory Neural Networks With Multiple Time-Varying Delays

被引：19

作者：

Sheng, Yin ^{[1
,2
]}

Zeng, Zhigang ^{[1
,2
]}

Huang, Tingwen ^{[3
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China

[2] Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China

[3] Texas A&M Univ Qatar, Dept Sci, Doha, Qatar

来源：

IEEE TRANSACTIONS ON CYBERNETICS | 2022年 / 52卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Bidirectional associative memory neural networks (BAMNNs); discrete-time delay; distributed time delay; global stability; EXPONENTIAL STABILITY; PERIODIC-SOLUTION; SYNCHRONIZATION; MULTISTABILITY; STABILIZATION; COEFFICIENTS;

D O I：

10.1109/TCYB.2020.3011581

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 investigates the global stability of bidirectional associative memory neural networks with discrete and distributed time-varying delays (DBAMNNs). By employing the comparison strategy and inequality techniques, global asymptotic stability (GAS) and global exponential stability (GES) of the underlying DBAMNNs are of concern in terms of p-norm (p >= 2). Meanwhile, GES of the addressed DBAMNNs is also analyzed in terms of 1-norm. When distributed time delay is neglected, the GES of the corresponding bidirectional associative memory neural networks is presented as an M-matrix, which includes certain existing outcomes as special cases. Two examples are finally provided to substantiate the validity of theories.

引用

页码：4095 / 4104

页数：10

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