Periodic dynamics for nonlocal Hopfield neural networks with random initial data

In this paper, a class of nonlocal Hopfield neural networks with random initial data is introduced, where the randomness may be of probability uncertainty. Sufficient conditions are derived to ensure the existence and globally exponential convergence of periodic solution for the addressed system in...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the Franklin Institute 2021-10, Vol.358 (16), p.8656-8677
Main Authors: Chen, Zhang, Yang, Dandan, Zhong, Shitao
Format: Article
Language:English
Subjects:
Neural networks
Nonlinear equations
Nonlinear systems
Probability
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a class of nonlocal Hopfield neural networks with random initial data is introduced, where the randomness may be of probability uncertainty. Sufficient conditions are derived to ensure the existence and globally exponential convergence of periodic solution for the addressed system in the frame of nonlinear expectation and linear expectation, respectively. Moreover, numerical examples are given to show the effectiveness of the obtained results.
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2021.08.040