Prediction of landing distance for civil aircraft

doi:10.16265/j.cnki.issn1003-3033.2017.01.014

Abstract

Abstract: In order to prevent aircraft from running out of runway, the paper was aimed at predicting the aircraft landing distance by means of an SVM model. B737-800 was taken as the reference type on the basis of considering specific factors influencing the distance, namely those relating to the airport, the weather and the aircraft. The operation data were collected by using Boeing LAND software. The radial basis function (RBF) kernel function was chosen by selecting the minimum error and the optimal accuracy. The best penalty function c and the kernel function parameter g were optimized by using grid parameters, genetic algorithm and particle swarm optimization algorithm. The results show that the prediction of landing distance conforms with the measured data, the maximum absolute error is 20 meters, and the maximum relative error is 1%.

Key words: flight safety, landing distance, support vector machine(SVM), regression prediction, genetic algorithm(GA), particle swarm optimization(PSO) algorithm

CLC Number:

X949

WEN Ruiying, WU Bo, CHU Shuanglei, WANG Hongyong. Prediction of landing distance for civil aircraft[J]. China Safety Science Journal, 2017, 27(1): 77-81.

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