A comparative study between artificial neural networks and support vector regression for modeling of the dissipated energy through tire-obstacle collision dynamics

H. Taghavifar, A. Mardani, H. Karim Maslak

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Energy dissipation control has long been synthesized addressing the trafficking of wheeled vehicles. Wheel-obstacle collision has attracted the studies more on ride comfort, stability, maneuvering, and suspension purposes. This paper communicates, for the first time, the energy dissipation analysis through tire-obstacle collision that frequently occurs for the wheeled vehicles particularly those of off-road vehicles. To this aim, a soil bin facility equipped with a single wheel-tester is employed considering input parameters of wheel load, speed, slippage, and obstacle height each at three different levels. In the next step, the potential of classic artificial neural networks was appraised against support vector regression with the two kernels of radial basis function and polynomial function. On account of performance metrics, it was revealed that radial basis function based support vector regression is outperforming the other tested methods for the prediction of dissipated energy through tire-obstacle collision dynamics. The details are documented in the paper.

Original languageEnglish
Pages (from-to)358-364
Number of pages7
JournalEnergy
Volume89
Early online date10 Jul 2015
DOIs
Publication statusPublished - Sep 2015
Externally publishedYes

Fingerprint

Tires
Wheels
Neural networks
Energy dissipation
Off road vehicles
Bins
Polynomials
Soils

Keywords

  • Energy dissipation
  • Off-road vehicles
  • ModelingSVR (support vector regression)
  • ANN (artificial neural network)

Cite this

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abstract = "Energy dissipation control has long been synthesized addressing the trafficking of wheeled vehicles. Wheel-obstacle collision has attracted the studies more on ride comfort, stability, maneuvering, and suspension purposes. This paper communicates, for the first time, the energy dissipation analysis through tire-obstacle collision that frequently occurs for the wheeled vehicles particularly those of off-road vehicles. To this aim, a soil bin facility equipped with a single wheel-tester is employed considering input parameters of wheel load, speed, slippage, and obstacle height each at three different levels. In the next step, the potential of classic artificial neural networks was appraised against support vector regression with the two kernels of radial basis function and polynomial function. On account of performance metrics, it was revealed that radial basis function based support vector regression is outperforming the other tested methods for the prediction of dissipated energy through tire-obstacle collision dynamics. The details are documented in the paper.",
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