A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles

H. Taghavifar, A. Mardani

Research output: Contribution to journalArticle

30 Citations (Scopus)


Machine dynamics and soil elastic–plastic characteristic sort out the soil-wheel interaction productions as very complex problem to be estimated. Energy dissipation due to motion resistance, as the most prominent performance index of towed wheels, is associated with soil properties and tire parameters. The objective of this study was to develop, for the first time, a model for prediction of energy loss in soil working machines using the datasets obtained from soil bin facility and a single-wheel tester. A total of 90 data points were derived from experimentations at five levels of wheel load (1, 2, 3, 4, and 5 kN), six tire inflation pressure (50, 100, 150, 200, 250, and 300 kPa) and three forward velocities (0.7, 1.4 and 2 m/s). ANN (Artificial neural network) was used for modeling of obtained results compared to the forecasting ability of SVR (support vector regression) technique. Several statistical criterions, (i.e. MAPE (mean absolute percentage error), MSE (mean square error), MRE (mean relative error) and coefficient of determination (R2) were incorporated in the investigations. It was observed, on the basis of statistical criterions, that SVR-based generalized model outperformed ANN in modeling energy loss and exhibited its applicability as a promising tool in this domain.
Original languageEnglish
Pages (from-to)569-576
Number of pages8
Early online date28 Jan 2014
Publication statusPublished - 1 Mar 2014
Externally publishedYes



  • Artificial neural network
  • Energy loss
  • Motion resistance
  • Soil bin
  • Support vector regression

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