本篇文章主要描述一套利用倒傳遞類神經網路技術原理為基礎而建立之車輛油耗預測系統。此研究包含三個主要部分，分別為油耗資料庫建立、燃油消耗預測的原理與類神經演算法則及預測性能的評估。在實際的車輛行駛狀況中，雖然有多種影響車輛油耗的因素，但本研究針對五個重要且明確的影響因素，將其做為類神經網路的輸入變因資料，這五個因素分別為汽車品牌、引擎排氣量大小、車重、車輛型式以及變速箱型式。並且將上述五個影響因素輸入倒傳遞類神經網路作為資料的特徵，進而獲得輸出資料，據以建立車輛油耗預估之系統。由預測結果與實際油耗資料比對顯示本研究所設計的類神經方法擁有不錯的學習及預測能力

A predict system for car fuel consumption using back-propagation neural network is design in this paper. The proposed system is constituted of three parts: fuel consumption information acquisition, fuel consumption
forecasting algorithm and performance evaluation. Although there are many factors will effect the fuel consumption of a car in a practice drive procedure, however, in the present system the impact factors for fuel consumption are simply decided as mark 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 predict system, an artificial neural network with back-propagation neural network is possession of 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.