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.
Similar content being viewed by others
References
McKeys E (1985) Soil cutting and tillage. Elsevier, Amsterdam
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
Zhang ZX, Kushwaha RL (1999) Application of neural networks to simulate soil tool interaction and soil behaviour. Can Agric Eng 41:119–125
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
Thompson LM, Kramer MA (1994) Modeling chemical processes using prior knowledge and neural networks. AIChE J 40:1328–1340
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
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
Haykin S (1994) Neural networks: a comprehensive foundation. McMillan College Publishing Company, New York
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-013-1360-8