Stability and Stabilization in Probability of Probabilistic Boolean Networks.


Journal

IEEE transactions on neural networks and learning systems
ISSN: 2162-2388
Titre abrégé: IEEE Trans Neural Netw Learn Syst
Pays: United States
ID NLM: 101616214

Informations de publication

Date de publication:
Jan 2021
Historique:
pubmed: 29 3 2020
medline: 29 3 2020
entrez: 29 3 2020
Statut: ppublish

Résumé

This article studies the stability in probability of probabilistic Boolean networks and stabilization in the probability of probabilistic Boolean control networks. To simulate more realistic cellular systems, the probability of stability/stabilization is not required to be a strict one. In this situation, the target state is indefinite to have a probability of transferring to itself. Thus, it is a challenging extension of the traditional probability-one problem, in which the self-transfer probability of the target state must be one. Some necessary and sufficient conditions are proposed via the semitensor product of matrices. Illustrative examples are also given to show the effectiveness of the derived results.

Identifiants

pubmed: 32217481
doi: 10.1109/TNNLS.2020.2978345
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

241-251

Auteurs

Classifications MeSH