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

Author

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  • Jose L. Salmeron

    (Data Science Lab, Universidad Pablo de Olavide, Ctra. de Utrera km. 1, 41013 Sevilla, Spain
    Universidad Autónoma de Chile, 5 Poniente, 1670 Talca, Chile)

  • Antonio Ruiz-Celma

    (Universidad de Extremadura, Avda. de Elvas s/n, 06006 Badajoz, Spain)

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 ).

Suggested Citation

  • Jose L. Salmeron & Antonio Ruiz-Celma, 2018. "Elliot and Symmetric Elliot Extreme Learning Machines for Gaussian Noisy Industrial Thermal Modelling," Energies, MDPI, vol. 12(1), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:90-:d:193718
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    References listed on IDEAS

    as
    1. Gejirifu De & Wangfeng Gao, 2018. "Forecasting China’s Natural Gas Consumption Based on AdaBoost-Particle Swarm Optimization-Extreme Learning Machine Integrated Learning Method," Energies, MDPI, vol. 11(11), pages 1-20, October.
    2. Karim, M.A. & Perez, E. & Amin, Z.M., 2014. "Mathematical modelling of counter flow v-grove solar air collector," Renewable Energy, Elsevier, vol. 67(C), pages 192-201.
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