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

Hamid Taghavifar, Aref Mardani, Ashkan Haji Hosseinloo

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1704-1710
Number of pages7
JournalEnergy
Volume93
Early online date19 Nov 2015
DOIs
Publication statusPublished - 15 Dec 2015
Externally publishedYes

Keywords

  • Artificial intelligence
  • Off-road vehicles
  • Power
  • Soil bin
  • Tractor

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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