Pan-Logical Probabilistic Algorithms Based on Convolutional Neural Networks.
Journal
Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357
Informations de publication
Date de publication:
2022
2022
Historique:
received:
05
06
2022
revised:
25
06
2022
accepted:
27
06
2022
entrez:
22
8
2022
pubmed:
23
8
2022
medline:
24
8
2022
Statut:
epublish
Résumé
A brand-new kind of flexible logic system called universal logic aims to address a variety of uncertain problems. In this study, the role of convolutional neural networks in assessing probabilistic pan-logic algorithms is investigated. A generic logic probability algorithm analysis based on a convolutional neural network is suggested due to the unpredictable outputs of the probabilistic algorithm and the difficulty of its analysis. The stochastic gradient descent technique and the error backpropagation algorithm are used to investigate the broad logic probability algorithm (SGD). The experimental data presented in this research show that the BP algorithm of the convolutional neural network has an accuracy rate of 89 percent when analysing the experimental data. As there are more experimental iterations, the error will go down. The SGD method proves that raising the algorithm's learning rate reduces the loss value of the function. The loss value can be as low as 100%, and the algorithm analysis is closer to the real.
Identifiants
pubmed: 35990166
doi: 10.1155/2022/8935906
pmc: PMC9385339
doi:
Types de publication
Journal Article
Retracted Publication
Langues
eng
Sous-ensembles de citation
IM
Pagination
8935906Commentaires et corrections
Type : RetractionIn
Informations de copyright
Copyright © 2022 Fangrong Liu.
Déclaration de conflit d'intérêts
The author does not have any possible conflicts of interest.
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