Quasi-bipartite synchronization of signed delayed neural networks under impulsive effects.

Delayed neural networks Disturbance Impulsive control Quasi-bipartite synchronization

Journal

Neural networks : the official journal of the International Neural Network Society
ISSN: 1879-2782
Titre abrégé: Neural Netw
Pays: United States
ID NLM: 8805018

Informations de publication

Date de publication:
Sep 2020
Historique:
received: 04 01 2020
revised: 19 04 2020
accepted: 11 05 2020
pubmed: 3 6 2020
medline: 28 11 2020
entrez: 3 6 2020
Statut: ppublish

Résumé

This paper mainly studies quasi-bipartite synchronization (QBPS) of signed delayed neural networks (SDNNs) under impulsive effects, in which the nodes have cooperative as well as antagonistic interactions. It is assumed that disturbance occurs in the communication channels between some neighboring agents at impulsive occurring instants. Under the balanced network topology, some sufficient criteria to achieve QBPS of SDNNs are proposed by utilizing algebraic graph theory and extended Halanay differential inequality. Moreover, for the QBPS error of SDNNs, the upper bound of the final error state is also provided explicitly. Two numerical examples are presented to demonstrate the correctness of the theoretical results.

Identifiants

pubmed: 32485559
pii: S0893-6080(20)30182-9
doi: 10.1016/j.neunet.2020.05.012
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

31-42

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Guohong Mu (G)

School of Mathematics, Hefei University of Technology, Hefei, 230009, China. Electronic address: leopold_2015@163.com.

Lulu Li (L)

School of Mathematics, Hefei University of Technology, Hefei, 230009, China. Electronic address: lululima@hfut.edu.cn.

Xiaodi Li (X)

School of Mathematics and Statistics, Shandong Normal University, Ji'nan 250014, China. Electronic address: lxd@sdnu.edu.cn.

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Classifications MeSH