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Appraisal of artificial neural network-genetic algorithm based model for prediction of the power provided by the agricultural tractors

Author

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  • Taghavifar, Hamid
  • Mardani, Aref
  • Hosseinloo, Ashkan Haji

Abstract

The knowledge of the available power provided by the driving wheel of agricultural tractors is required to gain a correct insight into the energy management of agricultural tractors. The design of the tractors is pivotal on the maximization of the traction efficiency and simultaneous minimization of energy dissipation. This paper spearheads the synthesis of the power provided by the agricultural tractors as affected by wheel load, slip and speed by use of the potential of a soil bin facility and a single-wheel test rig. The hybridized artificial neural network-genetic algorithm method was adopted to model the provided power of the driving wheel under the effect of the aforementioned tire parameters. The common drawback of the back-propagation algorithm known as the low speed of convergence and the possibility of being trapped in a local minimum was solved by the use of genetic algorithm. The mean square error equal to 0.02242 was obtained as the most optimal artificial neural network-genetic algorithm configuration using Levenberg–Marquardt training algorithm. Therefore, a 3-9-1 feed-forward with back propagation learning algorithm was selected as the modeling structure. The computed coefficient of determination for the training and test phases of the best artificial neural network-genetic algorithm model was obtained at 0.9696 and 0.9672, respectively. The present study spearheads the required power estimation for the driving wheels of off-road vehicles while the experimental test conduction in a controlled soil bin facility using single-wheel tester and adoption of soft computing tools are of the highlights and added values of the paper.

Suggested Citation

  • Taghavifar, Hamid & Mardani, Aref & Hosseinloo, Ashkan Haji, 2015. "Appraisal of artificial neural network-genetic algorithm based model for prediction of the power provided by the agricultural tractors," Energy, Elsevier, vol. 93(P2), pages 1704-1710.
    • Handle: RePEc:eee:energy:v:93:y:2015:i:p2:p:1704-1710
    DOI: 10.1016/j.energy.2015.10.066
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    References listed on IDEAS

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    1. Taghavifar, Hamid & Mardani, Aref, 2014. "Analyses of energy dissipation of run-off-road wheeled vehicles utilizing controlled soil bin facility environment," Energy, Elsevier, vol. 66(C), pages 973-980.
    2. 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.
    3. 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.
    4. 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.
    5. Taghavifar, Hamid & Mardani, Aref, 2014. "Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices," Energy, Elsevier, vol. 68(C), pages 651-657.
    6. 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.
    7. 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.
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    Cited by:

    1. Naji Mordi Naji Al-Dosary & Abdulwahed Mohamed Aboukarima & Saad Abdulrahman Al-Hamed, 2022. "Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables," Agriculture, MDPI, vol. 12(6), pages 1-24, June.
    2. 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.

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