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Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks

机译:利用人工神经网络预测汽油发动机的扭矩和比燃料消耗

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摘要

This study presents an artificial neural network (ANN) model to predict the torque and brake specific fuel consumption of a gasoline engine. An explicit ANN based formulation is developed to predict torque and brake specific fuel consumption of a gasoline engine in terms of spark advance, throttle position and engine speed. The proposed ANN model is based on experimental results. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. An ANN model based on a back-propagation learning algorithm for the engine was developed. The performance and an accuracy of the proposed ANN model are found satisfactory. This study demonstrates that ANN is very efficient for predicting the engine torque and brake specific fuel consumption. Moreover, the proposed ANN model is presented in explicit form as a mathematical function.
机译:这项研究提出了一种人工神经网络(ANN)模型来预测汽油发动机的扭矩和制动特定燃料消耗。开发了一种基于ANN的显式公式,以根据火花提前量,节气门位置和发动机转速来预测汽油发动机的扭矩和制动器特定燃料消耗。所提出的人工神经网络模型基于实验结果。完成了实验研究以获得训练和测试数据。在所有81个数据集中,训练和测试集分别由随机选择的63和18个集合组成。建立了基于反向传播学习算法的发动机神经网络模型。发现所提出的人工神经网络模型的性能和准确性令人满意。这项研究表明,人工神经网络可以非常有效地预测发动机扭矩和特定制动油耗。此外,所提出的人工神经网络模型以数学函数的形式显式表示。

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