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引用次数: 0
摘要
本文讨论了柴油机各种运行模式下的油耗神经模糊模型的建立。给出了根据现场试验结果训练的神经模糊网络的结构。为了训练神经网络，采用了误差反向传播算法和最小二乘法的混合方法。在训练过程中，选取的神经模糊网络参数使单个测量的最大误差为3.53375%，模型的平均误差为0.4429%。神经模糊柴油模型可以根据测试周期ETC EK UN No. 49模拟其各种运行模式。利用所获得的非定常工况下燃油消耗模型的优化方法，可以在标定电控单元时确定接近最优的柴油参数。
Neuro-Fuzzy Modeling of Diesel Fuel Consumption Under Dynamic Loads
The article discusses the construction of a neuro-fuzzy model of diesel fuel consumption in various modes of its operation. The structure of a neuro-fuzzy network trained according to the results of field tests is presented. To train the neural network, a hybrid method was used in the form of an error back propagation algorithm and a least squares method. In the process of training, the parameters of the neural-fuzzy network were selected that provide the maximum error of an individual measurement of 3.5375% with an average error of the model of 0.4429%. The neuro-fuzzy diesel model makes it possible to simulate various modes of its operation in accordance with the test cycle ETC EK UN No. 49. Using well-known optimization methods from the obtained model of fuel consumption under unsteady loads, it is possible to determine diesel parameters close to optimal when calibrating the electronic control unit.
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