Genetic algorithm with radial basis mapping network for the electricity consumption modeling

Israel Elias, José de Jesús Rubio, Dany Ivan Martinez, Tomas Miguel Vargas, Victor Garcia, Dante Mujica-Vargas, Jesus Alberto Meda-Campaña, Jaime Pacheco, Guadalupe Juliana Gutierrez, Alejandro Zacarias

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

© 2020 by the authors. The modified backpropagation algorithm based on the backpropagation with momentum is used for the parameters updating of a radial basis mapping (RBM) network, where it requires of the best hyper-parameters for more precise modeling. Seeking of the best hyper-parameters in a model it is not an easy task. In this article, a genetic algorithm is used to seek of the best hyper-parameters in the modified backpropagation for the parameters updating of a RBM network, and this RBM network is used for more precise electricity consumption modeling in a city. The suggested approach is called genetic algorithm with a RBM network. Additionally, since the genetic algorithm with a RBM network starts from the modified backpropagation, we compare both approaches for the electricity consumption modeling in a city.
Original languageAmerican English
JournalApplied Sciences (Switzerland)
DOIs
StatePublished - 1 Jun 2020

Fingerprint Dive into the research topics of 'Genetic algorithm with radial basis mapping network for the electricity consumption modeling'. Together they form a unique fingerprint.

Cite this