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Application of support vector machines in aircraft condition monitoring

FEI Li-xin,SONG Ji-xue, YU Xing-hua (The Engineering Institute,Air Force Engineering University,Xi'an 710038,China)  
Based on analysis to flight parameters,the theoretical basis for using Support Vector Machines(SVM) in time series prediction is described,and a general framework for time series prediction is introduced in detail.Then we used the SVM prediction model in the forecasting the flight parameters of a certain type of aero-engine,and compared it with the AR prediction model of time series.The conclusion is: compared with the general prediction method,SVM adopts a new type of structural risk minimization principle and owns excellent generalizing capability,long predictable area and high prediction precision,which is of important meanings for study on aircraft condition monitoring.
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