| Autor: |
Israel Elias, José de Jesús Rubio, David Ricardo Cruz, Genaro Ochoa, Juan Francisco Novoa, Dany Ivan Martinez, Samantha Muñiz, Ricardo Balcazar, Enrique Garcia, Cesar Felipe Juarez |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Applied Sciences, Vol 10, Iss 6, p 2036 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app10062036 |
| Popis: |
The steepest descent method is frequently used for neural network tuning. Mini-batches are commonly used to get better tuning of the steepest descent in the neural network. Nevertheless, steepest descent with mini-batches could be delayed in reaching a minimum. The Hessian could be quicker than the steepest descent in reaching a minimum, and it is easier to achieve this goal by using the Hessian with mini-batches. In this article, the Hessian is combined with mini-batches for neural network tuning. The discussed algorithm is applied for electrical demand prediction. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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