Artificial Neural Network and stepwise multiple range regression methods for prediction of tractor fuel consumption

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• 作者：Fatemeh  ; Rahimi-Ajdadi  ; ;   ; ^{rahimi_a61@yahoo.com} ; Yousef  ; Abbaspour-Gilandeh
• 关键词：Fuel consumption ; Neural network ; Stepwise regression ; Nebraska Tractor Test Lab
• 刊名：Measurement
• 出版年：2011
• 出版时间：December 2011
• 年：2011
• 卷：44
• 期：10
• 页码：2104-2111
• 全文大小：1319 K

文摘

Predicting tractor fuel consumption can lead to selection of the best conservation practices for farm equipments. In present study for estimating tractor fuel consumption was used from the Nebraska Tractor Test Lab (NTTL) data. Fuel consumption was assumed to be a function of engine speed, throttle and load conditions, chassis type, total tested weight, drawbar and PTO power. Back propagation Artificial Neural Network (ANN) models with six training algorithms were adopted for predicting fuel consumption. The highest performance was obtained for the network with two hidden layers each having 10 neurons which employed Levenberg¨CMarquardt training algorithm. Results indicated that the ANN and stepwise regression models represented similar determination coefficient (R² = 0.986 and R² = 0.973, respectively) while the ANN provided relatively better prediction accuracy (R² = 0.938) compared to stepwise regression (R² = 0.910). One of the advantages of ANN model was integration of load and throttle condition in the form of one model.