Abstract
A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors which will effect the fuel consumption of a car in a practical drive procedure, however, in the present system the impact 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 the fuel consumption forecasting, to verify the effect of the proposed predictive system, an artificial neural network with back-propagation neural network has a learning capability for car fuel consumption prediction. The prediction results demonstrated that the proposed system using neural network is effective and the performance is satisfactory in fuel consumption prediction.
| Original language | English |
|---|---|
| Pages (from-to) | 4967-4971 |
| Number of pages | 5 |
| Journal | Expert Systems with Applications |
| Volume | 38 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2011 May 1 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Engineering(all)
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Development of a predictive system for car fuel consumption using an artificial neural network. / Wu, Jian-Da; Liu, Jun Ching.
In: Expert Systems with Applications, Vol. 38, No. 5, 01.05.2011, p. 4967-4971.Research output: Contribution to journal › Article
TY - JOUR
T1 - Development of a predictive system for car fuel consumption using an artificial neural network
AU - Wu, Jian-Da
AU - Liu, Jun Ching
PY - 2011/5/1
Y1 - 2011/5/1
N2 - A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors which will effect the fuel consumption of a car in a practical drive procedure, however, in the present system the impact 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 the fuel consumption forecasting, to verify the effect of the proposed predictive system, an artificial neural network with back-propagation neural network has a learning capability for car fuel consumption prediction. The prediction results demonstrated that the proposed system using neural network is effective and the performance is satisfactory in fuel consumption prediction.
AB - A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors which will effect the fuel consumption of a car in a practical drive procedure, however, in the present system the impact 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 the fuel consumption forecasting, to verify the effect of the proposed predictive system, an artificial neural network with back-propagation neural network has a learning capability for car fuel consumption prediction. The prediction results demonstrated that the proposed system using neural network is effective and the performance is satisfactory in fuel consumption prediction.
UR - http://www.scopus.com/inward/record.url?scp=79151477968&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79151477968&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2010.09.155
DO - 10.1016/j.eswa.2010.09.155
M3 - Article
AN - SCOPUS:79151477968
VL - 38
SP - 4967
EP - 4971
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 5
ER -