On the modeling of energy efficiency indices of agricultural tractor driving wheels applying adaptive neuro-fuzzy inference system

H. Taghavifar, A. Mardani

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

The objective is to assess the potential of adaptive neuro-fuzzy inference system (ANFIS) for the prediction of energy efficiency indices of driving wheels (i.e. traction coefficient and tractive power efficiency). The output parameters were evaluated as affected by the tire parameters of wheel load at three different levels, velocity at three different levels and slippage at three different levels with three replications forming a total of 81 data points. ANFIS with a hybrid method of the gradient descent and the least-squares method was applied to find the optimal learning parameters using various membership functions (MFs). Statistical performance parameters of mean square error (MSE) and coefficient of determination, R2, were considered as the modeling evaluation criteria. The implementations divulged that Gaussian membership function (gaussmf) and Trapezoidal membership function (tramf) configurations were found to denote MSE of 0.0166 and R2 of 0.98 for traction coefficient while MSE equal to 1.5676 and R2 equal to 0.97 for the tractive power efficiency were obtained.
Original languageEnglish
Pages (from-to)37-47
Number of pages11
JournalJournal of Terramechanics
Volume56
Early online date10 Sep 2014
DOIs
Publication statusPublished - Dec 2014
Externally publishedYes

Fingerprint

Tractors (agricultural)
Fuzzy inference
Membership functions
Mean square error
Energy efficiency
Wheels
Tires

Keywords

  • ANFIS
  • Energy efficiency
  • Traction coefficient
  • Tractive power efficiency

Cite this

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abstract = "The objective is to assess the potential of adaptive neuro-fuzzy inference system (ANFIS) for the prediction of energy efficiency indices of driving wheels (i.e. traction coefficient and tractive power efficiency). The output parameters were evaluated as affected by the tire parameters of wheel load at three different levels, velocity at three different levels and slippage at three different levels with three replications forming a total of 81 data points. ANFIS with a hybrid method of the gradient descent and the least-squares method was applied to find the optimal learning parameters using various membership functions (MFs). Statistical performance parameters of mean square error (MSE) and coefficient of determination, R2, were considered as the modeling evaluation criteria. The implementations divulged that Gaussian membership function (gaussmf) and Trapezoidal membership function (tramf) configurations were found to denote MSE of 0.0166 and R2 of 0.98 for traction coefficient while MSE equal to 1.5676 and R2 equal to 0.97 for the tractive power efficiency were obtained.",
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