On a heavy-tailed distribution and the stability of an equilibrium in a distributed delay symmetric network

被引：0

作者：

Ncube, Israel ^{[1
]}

机构：

[1] Alabama A&M Univ, Coll Engn Technol & Phys Sci, Dept Math, 4900 Meridian St North, Huntsville, AL 35762 USA

来源：

CHAOS SOLITONS & FRACTALS | 2021年 / 152卷

关键词：

Static neural networks; S-type distributed time delays; Pareto distribution; Equilibrium; Stability; NEURAL-NETWORK; DIFFERENTIAL-EQUATION; GLOBAL STABILITY; MULTIPLE; REGION; MODELS;

D O I：

10.1016/j.chaos.2021.111330

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

We consider a static artificial neural network model endowed with multiple unbounded S-type dis-tributed time delays. The delay kernels are described by the Pareto distribution, which is a heavy-tailed power-law probability distribution frequently employed in the characterisation of many observable phe-nomena. We give a characterisation of the effects of the shape and the scale of the Pareto delay distribu-tion on the stability of an equilibrium of the network. (c) 2021 Elsevier Ltd. All rights reserved.

引用

页数：9

共 22 条

[1]

Anderson R., 1993, J Dynam Differential Equations, V5, P105

[2] GEOMETRIC AND PROBABILISTIC STABILITY-CRITERIA FOR DELAY SYSTEMS [J].