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A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles

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  • Taghavifar, Hamid
  • Mardani, Aref

Abstract

Machine dynamics and soil elastic–plastic characteristic sort out the soil-wheel interaction productions as very complex problem to be estimated. Energy dissipation due to motion resistance, as the most prominent performance index of towed wheels, is associated with soil properties and tire parameters. The objective of this study was to develop, for the first time, a model for prediction of energy loss in soil working machines using the datasets obtained from soil bin facility and a single-wheel tester. A total of 90 data points were derived from experimentations at five levels of wheel load (1, 2, 3, 4, and 5 kN), six tire inflation pressure (50, 100, 150, 200, 250, and 300 kPa) and three forward velocities (0.7, 1.4 and 2 m/s). ANN (Artificial neural network) was used for modeling of obtained results compared to the forecasting ability of SVR (support vector regression) technique. Several statistical criterions, (i.e. MAPE (mean absolute percentage error), MSE (mean square error), MRE (mean relative error) and coefficient of determination (R2) were incorporated in the investigations. It was observed, on the basis of statistical criterions, that SVR-based generalized model outperformed ANN in modeling energy loss and exhibited its applicability as a promising tool in this domain.

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  • Taghavifar, Hamid & Mardani, Aref, 2014. "A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles," Energy, Elsevier, vol. 66(C), pages 569-576.
    • Handle: RePEc:eee:energy:v:66:y:2014:i:c:p:569-576
    DOI: 10.1016/j.energy.2014.01.022
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    References listed on IDEAS

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    Cited by:

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    2. Taghavifar, Hamid & Mardani, Aref & Karim Maslak, Haleh, 2015. "A comparative study between artificial neural networks and support vector regression for modeling of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 89(C), pages 358-364.
    3. Can, Özer & Baklacioglu, Tolga & Özturk, Erkan & Turan, Onder, 2022. "Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel," Energy, Elsevier, vol. 247(C).
    4. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    5. Chetan Badgujar & Sanjoy Das & Dania Martinez Figueroa & Daniel Flippo, 2023. "Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review," Agriculture, MDPI, vol. 13(2), pages 1-39, January.
    6. Taghavifar, Hamid & Mardani, Aref & Hosseinloo, Ashkan Haji, 2015. "Experimental analysis of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 91(C), pages 573-578.
    7. Taghavifar, Hamid & Mardani, Aref, 2015. "Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system," Energy, Elsevier, vol. 85(C), pages 586-593.
    8. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha & Wenying Wen, 2019. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods," Energies, MDPI, vol. 12(9), pages 1-17, May.
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    11. Taghavifar, Hamid & Mardani, Aref & Karim-Maslak, Haleh, 2014. "Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility," Energy, Elsevier, vol. 73(C), pages 762-770.
    12. Cao, Li-hua & Yu, Jing-wen & Li, Yong, 2016. "Study on the determination method of the normal value of relative internal efficiency of the last stage group of steam turbine," Energy, Elsevier, vol. 98(C), pages 101-107.
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