@inproceedings{55cf615df00845fdbae8691daa75e648,
title = "An improved aircraft landing distance prediction model based on particle swarm optimization - Extreme learning machine method",
abstract = "Aiming at the problem that aircraft landing runway overrun, this paper proposed a landing distance prediction model based on improved extreme learning machine (ELM) with flight data. Particle swarm optimization (PSO) was used to optimize the input layer weights and the hidden element bias of a single hidden layer feedforward network. And then the optimal input weights and the implicit bias were applied to the ELM prediction model. Firstly, flight data is preprocessed with data slicing method based on flight height, and determine model input variables. Secondly, select the appropriate activation function. Subsequently, establish the PSO-ELM model of landing distance prediction. In the end, compare with traditional BP neural network and ELM under different evaluation indexes. The results show that the prediction of landing distance conforms to the actual measured data. The maximum absolute error is 45 meters, and the maximum relative error is 6\%.",
keywords = "Landing Distance, PSO-ELM, aircraft, flight data, flight safety",
author = "Silin Qian and Shenghan Zhou and Wenbing Chang and Fajie Wei",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 ; Conference date: 10-12-2017 Through 13-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/IEEM.2017.8290307",
language = "英语",
series = "IEEE International Conference on Industrial Engineering and Engineering Management",
publisher = "IEEE Computer Society",
pages = "2326--2330",
booktitle = "2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017",
address = "美国",
}