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Elias, Israel; Rubio, José de Jesús; Cruz, David Ricardo; Ochoa, Genaro; Novoa, Juan Francisco; Martinez, Dany Ivan; Muñiz, Samantha; Balcazar, Ricardo; Garcia, Enrique; Juarez, Cesar Felipe

Hessian with Mini-Batches for Electrical Demand Prediction

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  • Medientyp: E-Artikel
  • Titel: Hessian with Mini-Batches for Electrical Demand Prediction
  • Beteiligte: Elias, Israel; Rubio, José de Jesús; Cruz, David Ricardo; Ochoa, Genaro; Novoa, Juan Francisco; Martinez, Dany Ivan; Muñiz, Samantha; Balcazar, Ricardo; Garcia, Enrique; Juarez, Cesar Felipe
  • Erschienen: MDPI AG, 2020
  • Erschienen in: Applied Sciences, 10 (2020) 6, Seite 2036
  • Sprache: Englisch
  • DOI: 10.3390/app10062036
  • ISSN: 2076-3417
  • Schlagwörter: Fluid Flow and Transfer Processes ; Computer Science Applications ; Process Chemistry and Technology ; General Engineering ; Instrumentation ; General Materials Science
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:p>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.</jats:p>
  • Zugangsstatus: Freier Zugang