民航飞机的快速存取记录仪(QAR)在飞行过程中记录了大量的飞行参数,QAR数据是飞行安全评估的重要依据。针对QAR数据大样本、高维度的特点,提出了一种有效的飞行数据特征提取的高效算法——DBN算法。DBN优势在于其能够摆脱对大量数据处理技术与专家经验的依赖而对飞行数据进行特征提取。在不同类别飞行数据集上进行仿真实验,结果显示与主成分分析法(PCA)相比,通过DBN提取的特征进行分类识别准确率更高。

A great number of flight parameters are recorded by the Quick Access Recorder (QAR) equipped on civil aircrafts.QAR data is an important criterion for flight safety assessment.Aiming at large-sample and high-dimension features of flight data from QAR,this paper proposes an effective feature extraction algorithm,Deep Belief Network (DBN) algorithm.The DBN algorithm can adaptively extract the features of flight data independent of data-processing technologies and expert experiences.Simulations of different types of flight data sets are carried out.The simulation results show that,compared with the PCA algorithm,the accuracy of classification and identification of features extracted by DBN model is higher.