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

doi:10.3390/app10124239

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

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

33 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Número de artículo	4239
Publicación	Applied Sciences (Switzerland)
Volumen	10
N.º	12
DOI	https://doi.org/10.3390/app10124239
Estado	Publicada - 1 jun. 2020

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title = "Genetic algorithm with radial basis mapping network for the electricity consumption modeling",

abstract = "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.",

keywords = "Backpropagation, Electricity consumption, Genetic algorithm, RBM network",

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T1 - Genetic algorithm with radial basis mapping network for the electricity consumption modeling

AU - Elias, Israel

AU - de Jesús Rubio, José

AU - Martinez, Dany Ivan

AU - Vargas, Tomas Miguel

AU - Garcia, Victor

AU - Mujica-Vargas, Dante

AU - Meda-Campaña, Jesus Alberto

AU - Pacheco, Jaime

AU - Gutierrez, Guadalupe Juliana

AU - Zacarias, Alejandro

PY - 2020/6/1

Y1 - 2020/6/1

N2 - 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.

AB - 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.

KW - Backpropagation

KW - Electricity consumption

KW - Genetic algorithm

KW - RBM network

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U2 - 10.3390/app10124239

DO - 10.3390/app10124239

M3 - Artículo

SN - 2076-3417

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JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

IS - 12

M1 - 4239

ER -

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

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