论文部分内容阅读
从航空发动机的使用经济性角度出发,对其主要使用经济性指标耗油率,提出了基于主成分分析与BP神经网络的航空发动机使用经济性建模方法。利用主成分分析从耗油率相关因素中提取主特征分量,消除样本间的相关性,降低BP网络的规模。接着,利用BP网络高度非线性映射能力构建模型。最后通过实例,验证了该模型的有效性,从而为涡扇发动机的方案设计提供了一种新的选择方法。
From the point of economy of aeroengine, this paper proposes an economic modeling method using aeroengine based on principal component analysis and BP neural network. Principal component analysis is used to extract the main characteristic components from the correlation coefficients of fuel consumption rate to eliminate the correlation between samples and reduce the size of BP network. Then, using BP network highly nonlinear mapping ability to build the model. Finally, an example is given to verify the effectiveness of the model, which provides a new selection method for the turbofan engine design.