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Use of artificial neural networks for estimation of agricultural wheel traction force in soil bin

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Abstract

This paper presents an application of artificial neural networks (ANNs) for the prediction of traction force using readily available datasets experimentally obtained from a soil bin utilizing single-wheel tester. Aiming this, firstly the tests were carried out using two soil textures and two tire types as affected by velocity, slippage, tire inflation pressure, and wheel load. On this basis, the potential of neural modeling was assessed with multilayered perceptron networks using various training algorithms among which, backpropagation algorithm was compared to backpropagation with declining learning rate factor algorithm due to their primarily yielded superior performance. The results divulged that the latter one could better achieve the aim of study in terms of performance criteria. Furthermore, it was inferred that ANNs could reliably provide a promising tool for prediction of traction force and its modeling.

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References

  1. McKeys E (1985) Soil cutting and tillage. Elsevier, Amsterdam

    Google Scholar 

  2. Roul AK, Raheman H, Pansare MS, Machavaram R (2009) Predicting the draught requirement of tillage implements in sandy clay loam soil using an artificial neural network. Biosyst Eng 104:476–485

    Article  Google Scholar 

  3. Zhang ZX, Kushwaha RL (1999) Application of neural networks to simulate soil tool interaction and soil behaviour. Can Agric Eng 41:119–125

    Google Scholar 

  4. Mardani A, Shahidi K, Rahmani A, Mashoofi B, Karimmaslak H (2010) Studies on a long soil bin for soil-tool interaction. Cercetări Agronomice în Moldova 142:5–10

    Google Scholar 

  5. Thompson LM, Kramer MA (1994) Modeling chemical processes using prior knowledge and neural networks. AIChE J 40:1328–1340

    Article  Google Scholar 

  6. Vakil-Baghmisheh MT, Pavesic N (2001) Back-propagation with declining learning rate. In: Proceeding of the 10th Electrotechnical and Computer Science Conference, Portoroz, Slovenia, vol B, pp 297–300

  7. Weiss M, Baret F, Leroy M, Hautecoeur O, Prevot L, Bruguier N (2000) Validation of neural network techniques for the estimation of canopy biophysical variables from vegetation data. Vegetation-2000, Lake Maggiore, Italy

  8. Haykin S (1994) Neural networks: a comprehensive foundation. McMillan College Publishing Company, New York

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agriculture, Urmia University, Nazloo Road, 571531177, Urmia, Iran

    Hamid Taghavifar & Aref Mardani

Authors
  1. Hamid Taghavifar
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  2. Aref Mardani
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Correspondence to Hamid Taghavifar.

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Taghavifar, H., Mardani, A. Use of artificial neural networks for estimation of agricultural wheel traction force in soil bin. Neural Comput & Applic 24, 1249–1258 (2014). https://doi.org/10.1007/s00521-013-1360-8

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  • DOI: https://doi.org/10.1007/s00521-013-1360-8

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