Elliot and Symmetric Elliot Extreme Learning Machines for Gaussian Noisy Industrial Thermal Modelling

Jose L. Salmeron and Antonio Ruiz-Celma
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Jose L. Salmeron: Data Science Lab, Universidad Pablo de Olavide, Ctra. de Utrera km. 1, 41013 Sevilla, Spain
Antonio Ruiz-Celma: Universidad de Extremadura, Avda. de Elvas s/n, 06006 Badajoz, Spain

Energies, 2018, vol. 12, issue 1, 1-19

Abstract: 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 ).

Keywords: extreme learning machines; machine learning; industrial drying; Gaussian noise (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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