Global exponential stability of discrete-time Hopfield neural network models with unbounded delays

被引：3
|
作者：
Oliveira, Jose J. [1 ]
机构：
[1] Univ Minho, Ctr Matemat CMAT, Escola Ciencias, P-4710057 Braga, Portugal
来源：
JOURNAL OF DIFFERENCE EQUATIONS AND APPLICATIONS | 2022年 / 28卷 / 05期
关键词：
Neural networks; delay difference equations; unbounded delays; global stability; BOUNDEDNESS; BEHAVIOR; NEURONS; ANALOGS;
D O I：
10.1080/10236198.2022.2073820
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, a general setting is presented to study the exponential stability of discrete-time systems with bounded or unbounded delays. Based on the M-matrix theory, we establish sufficient conditions to ensure the global exponential stability of the zero equilibrium of low-order, and high-order, discrete-time Hopfield neural network models with unbounded delays and delay in the leakage terms. A comparison of the literature shows that our results generalize and improve some in recent publications.
引用
收藏
页码：725 / 751
页数：27
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