On convergence properties of the brain-state-in-a-convex-domain.
Brain-State-in-a-Box neural network
Convergence
Discrete-time neural network
LaSalle’s invariance principle
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:
21 Jun 2024
21 Jun 2024
Historique:
received:
11
01
2024
revised:
14
05
2024
accepted:
19
06
2024
medline:
1
7
2024
pubmed:
1
7
2024
entrez:
30
6
2024
Statut:
aheadofprint
Résumé
Convergence in the presence of multiple equilibrium points is one of the most fundamental dynamical properties of a neural network (NN). Goal of the paper is to investigate convergence for the classic Brain-State-in-a-Box (BSB) NN model and some of its relevant generalizations named Brain-State-in-a-Convex-Body (BSCB). In particular, BSCB is a class of discrete-time NNs obtained by projecting a linear system onto a convex body of R
Identifiants
pubmed: 38945117
pii: S0893-6080(24)00405-2
doi: 10.1016/j.neunet.2024.106481
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
106481Informations de copyright
Copyright © 2024 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.