DocumentCode :
1781898
Title :
Incremental Support Vector Machine Learning Method for Aircraft Event Recognition
Author :
Xuhui Wang ; Ping Shu
Author_Institution :
China Acad. of Civil Aviation Sci. & Technol., Beijing, China
fYear :
2014
fDate :
2-3 Aug. 2014
Firstpage :
201
Lastpage :
204
Abstract :
Event indentification of hard landing is the hot spot in civil aviation safety research. In this paper, a incremental model to indentify aircraft land status of civil aircraft is presented to support fault diagnosis and structure maintenance. In previous reserach, traditional artificial neural network is used as a classifier for event detection from certain landing parameters. This paper develop a further recognition model by introducing support vector method, also an incremental algorithm is proposed to solve the problem of on line sample array, and sensitivity and specificity are employed to show the model performance comparing to existing model. Finally, advantage of this method is analysed, and the aspects of each model are given.
Keywords :
aerospace computing; air safety; aircraft landing guidance; fault diagnosis; learning (artificial intelligence); neural nets; pattern classification; support vector machines; aircraft event recognition; aircraft land status; artificial neural network; civil aircraft; civil aviation safety; classifier; event detection; event identification; fault diagnosis; hard landing; incremental support vector machine learning method; landing parameters; structure maintenance; Aircraft; Atmospheric modeling; Data models; Kernel; Load modeling; Support vector machines; Training; civil aircraft; hard landing event; incremental learning; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Enterprise Systems Conference (ES), 2014
Conference_Location :
Shanghai
Print_ISBN :
978-1-4799-5553-4
Type :
conf
DOI :
10.1109/ES.2014.14
Filename :
6997044
Link To Document :
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