Mostrar el registro sencillo del ítem

Elliot and symmetric elliot extreme learning machines for Gaussian noisy industrial thermal modelling

dc.contributor.authorSalmeron J.L.
dc.contributor.authorRuiz-Celma A.
dc.date.accessioned2020-09-02T22:27:43Z
dc.date.available2020-09-02T22:27:43Z
dc.date.issued2019
dc.identifier10.3390/en12010090
dc.identifier.citation12, 1, -
dc.identifier.issn19961073
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6169
dc.descriptionThis research proposes an Elliot-based Extreme Learning Machine approach for industrial thermal processes regression. The main contribution of this paper is to propose an Extreme Learning Machine model with Elliot and Symmetric Elliot activation functions that will look for the fittest number of neurons in the hidden layer. The methodological proposal is tested on an industrial thermal drying process. The thermal drying process is relevant in many industrial processes such as the food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries and others. The methodological proposal of this paper outperforms the following techniques: Linear Regression, k-Nearest Neighbours regression, Regression Trees, Random Forest and Support Vector Regression. In addition, all the experiments have been benchmarked using four error measurements (MAE, MSE, MEADE, R 2 ). © 2018 by the authors.
dc.language.isoen
dc.publisherMDPI AG
dc.subjectExtreme learning machines
dc.subjectGaussian noise
dc.subjectIndustrial drying
dc.subjectMachine learning
dc.subjectDecision trees
dc.subjectDrying
dc.subjectGaussian noise (electronic)
dc.subjectIndustrial research
dc.subjectKnowledge acquisition
dc.subjectLearning systems
dc.subjectNearest neighbor search
dc.subjectRegression analysis
dc.subjectSoaps (detergents)
dc.subjectTextile industry
dc.subjectActivation functions
dc.subjectBiofuels production
dc.subjectError measurements
dc.subjectExtreme learning machine
dc.subjectIndustrial processs
dc.subjectK-nearest neighbours
dc.subjectPharmaceutical industry
dc.subjectSupport vector regression (SVR)
dc.subjectThermal processing (foods)
dc.titleElliot and symmetric elliot extreme learning machines for Gaussian noisy industrial thermal modelling
dc.typeArticle


Ficheros en el ítem

Thumbnail
Nombre:
item_2-s2.0-85060015866.pdf
Tamaño:
2.636Kb
Formato:
PDF
Ver/

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem