A Decision Support System Based on Support Vector Machine for Hard Landing of Civil Aircraft
Hard landing event affect the flight safety seriously. In this paper, a decision support system that classifiers the hard landing signals of the civil aircraft to two classes (normal and abnormal) is presented to support fault diagnosis. As our previous paper where ANN is used as a classifier for event detection from measured hard landing signals. In this paper, our aim is to develop our previous work by using least-squares support vector machine (LS-SVM) classifier instead of ANN. We compare LS-SVM with backpropagation artificial neural network (BPANN) to classify the extracted features. In addition, we use receiver operator characteristic (ROC) curves to compare sensitivities and specificities of these classifiers and compute the area under the curves. Finally, performance of models are analysed, and the aspects of each model are given.
hard landing civil aircraft Support vector machine decision support system
Wang Xu-hui Shu Ping Rong Xiang Nie Lei
Aviation Safety Institute,Center of Aviation Safety Technology, CAAC,Beijing, China College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing, China
国际会议
重庆
英文
548-552
2009-12-25（万方平台首次上网日期，不代表论文的发表时间）