NDLI: A Decision Support System Based on Support Vector Machine for Hard Landing of Civil Aircraft

Content Provider	IEEE Xplore Digital Library
Author	Wang Xu-hui Shu Ping Rong Xiang Nie Lei
Copyright Year	2009
Abstract	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 (BP-ANN) 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.
Starting Page	66
Ending Page	70
File Size	304824
Page Count	5
File Format	PDF
ISBN	9780769539300
e-ISBN	9781424454235
DOI	10.1109/IFCSTA.2009.137
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision support systems Backpropagation Event detection Decision support system Artificial neural networks Support vector machine Support vector machines Fault diagnosis Civil aircraft Hard landing Support vector machine classification Feature extraction Aerospace safety Aircraft
Content Type	Text
Resource Type	Article

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