Evaluation of artificial neural networks and the ARIMA model applied to temperature prediction in a charcoal oven

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dc.creatorRogério Santos Maciel
dc.creatorNilton Alves Maia
dc.creatorMaurílio José Inácio
dc.creatorFernando Colen
dc.creatorSidney Pereira
dc.creatorLuiz Henrique de Souza
dc.date.accessioned2024-09-13T11:15:17Z
dc.date.accessioned2025-09-08T23:50:30Z
dc.date.available2024-09-13T11:15:17Z
dc.date.issued2023
dc.identifier.issn2411-5681
dc.identifier.urihttps://hdl.handle.net/1843/76413
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInternational Journal of Education and Research
dc.rightsAcesso Aberto
dc.subjectRedes neurais (Computação)
dc.subjectCarvão vegetal
dc.subjectControle de temperatura
dc.subject.otherAutomatic temperature forecast
dc.subject.otherMethods
dc.subject.otherPerformance
dc.titleEvaluation of artificial neural networks and the ARIMA model applied to temperature prediction in a charcoal oven
dc.typeArtigo de periódico
local.citation.epage58
local.citation.issue3
local.citation.spage43
local.citation.volume11
local.description.resumoThis work used the Artificial Neural Networks RBF- Radial Basis Function and MLP- Multilayer Perceptron, the Neurofuzzy ANFIS- Adaptive-network-based Fuzzy Inference System and the model ARIMA-Auto Regressive Integrated Moving Average, to predict the temperature. Different topologies were tested for Artificial Neural Networks and also for Neurofuzzy, in order to find the best configuration. To evaluate the performance of the predictors, the following metrics were used: Root Mean Square Error - RMSE, Mean Square Error - MSE and Mean Absolute Error - MAE. The analysis of the quality of the predictors was also performed using Theil's U coefficient. Analyzing the results of this study, it was possible to verify that the Artificial Neural Networks, the Neurofuzzy Network and the ARIMA model, can be applied to predict the temperature in charcoal kilns, with the MLP and ANFIS networks presenting the best results.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttp://www.ijern.com/journal/2023/March-2023/04.pdf

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