A forecasting system for car fuel consumption using a radial basis function neural network

Jian Da Wu, Jun Ching Liu

Research output: Contribution to journalArticle

54 Citations (Scopus)

Abstract

A predictive system for car fuel consumption using a radial basis function (RBF) neural network is proposed in this paper. The proposed work consists of three parts: information acquisition, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors affecting the fuel consumption of a car in a practical drive procedure, in the present system the relevant factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In fuel consumption forecasting, to verify the effect of the proposed RBF neural network predictive system, an artificial neural network with a back-propagation (BP) neural network is compared with an RBF neural network for car fuel consumption prediction. The prediction results demonstrated the proposed system using the neural network is effective and the performance is satisfactory in terms of fuel consumption prediction.

Original languageEnglish
Pages (from-to)1883-1888
Number of pages6
JournalExpert Systems with Applications
Volume39
Issue number2
DOIs
Publication statusPublished - 2012 Feb 1

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All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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