神经网络内燃机排放模型学习样本的选定

Selection of the Learning Samples of Neural Networks for Internal-Combustion Engine Emission

研究神经网络理论用于内燃机排放预测模型时学习样本的选取方法,针对内燃机工况变化的特点,对传统的正交设计法进行了改进,提出并验证了用考虑因素边界变化的正交设计法选取样本的可行性。研究结果表明,模型的预测精度随着正交表位级的增加而提高,即使只用3位级的正交表设计样本,也能建立预测误差低于5.7%的内燃机稳态排放特性预测模型,具有试验工作量小、简便易行的特点。

According to the variation characteristics of the operating condition of internalcombustion engines, an improved orthogonal design method which considers the changes on factor boundary is proposed to choose learning samples of neural networks. The results show that the forecasting precision of the emission models built on 6135ZG diesel engine under stable operation increases with the increase of the level of the diagonal intersection table. Even if the sample is a diagonal intersection of level 3, the emissionforecasting model can be built with an error less than 5.7%. Compared with random selection of samples, the improved orthogonal design method is characterized by easiness and less work load.