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

页码：725 / 751

页数：27

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