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

H. Taghavifar, A. Mardani

Research output: Contribution to journalArticle

15 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1249-1258
Number of pages10
JournalNeural Computing and Applications
Volume24
Issue number6
Early online date12 Feb 2013
DOIs
Publication statusPublished - May 2014
Externally publishedYes

Fingerprint

Bins
Wheels
Neural networks
Soils
Tires
Backpropagation algorithms
Backpropagation
Textures

Keywords

  • Artificial neural networks
  • Backpropagation
  • Learning rate
  • Momentum
  • Soil bin
  • Traction force

Cite this

Use of artificial neural networks for estimation of agricultural wheel traction force in soil bin. / Taghavifar, H.; Mardani, A.

In: Neural Computing and Applications, Vol. 24, No. 6, 05.2014, p. 1249-1258.

Research output: Contribution to journalArticle

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