Elliot and symmetric elliot extreme learning machines for Gaussian noisy industrial thermal modelling
MetadataShow full item record
This 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.
Showing items related by title, author, creator and subject.
Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes (2020) Salmeron J.L.; Ruiz-Celma A.; Mena A. (Elsevier B.V., 2017)
Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition (2020) Villar J.R.; Vergara P.; Menéndez M.; De La Cal E.; González V.M.; Sedano J. (World Scientific Publishing Co. Pte Ltd, 2016)
ArticleSalmeron J.L.; Rahimi S.A.; Navali A.M.; Sadeghpour A. (Elsevier B.V., 2017)