Shallow Fully Connected Neural Network Training by Forcing Linearization into Valid Region and Balancing Training Rates
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Le résumé fourni par la source
A new supervisory training rule for a shallow fully connected neural network (SFCNN) is proposed in this present study. The proposed training rule is developed based on local linearization and analytical optimal solutions for linearized SFCNN. The cause of nonlinearity in neural network training is analyzed, and it is removed by local linearization. The optimal solution for the linearized SFCNN, which minimizes the cost function for the training, is analytically derived. Additionally, the training efficiency and model accuracy of the trained SFCNN are improved by keeping estimates within a valid range of the linearization. The superiority of the proposed approach is demonstrated by applying the proposed training rule to the modeling of a typical nonlinear pH process, Boston housing prices dataset, and automobile mileage per gallon dataset. The proposed training rule shows the smallest modeling error and the smallest iteration number required for convergence compared with several previous approaches from the literature for the case study.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Shallow Fully Connected Neural Network Training by Forcing Linearization into Valid Region and Balancing Training Rates
- Date Crossref
- 09/06/2022
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Kyungpook National University Department of Chemical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Sunchon National University Department of Chemical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Kyung Hee University Department of Environmental Engineering pays non établi dans la noticeUniversité ou école supérieure
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Korea University Department of Chemical and Biological Engineering pays non établi dans la noticeUniversité ou école supérieure
Department of Chemical Engineering — Kyungpook National University, Department of Chemical Engineering — Sunchon National University et Department of Environmental Engineering — Kyung Hee University, avec 1 autre affiliation.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.