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

被引:18
|
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
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
收藏
页码:4095 / 4104
页数:10
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