@inproceedings{9228e8d6531f49d9aef4db1547002356,
title = "A method of F-18/A carrier landing position prediction based on back propagation neural network",
abstract = "Carrier landing is the accident prone phase during carrier-based aircraft task. The prediction of carrier landing position is able to provide pilots with decision basis while various guidance modes represented by automatic carrier landing system (ACLS) are available. Air wake and deck heaving motion are both crucial factors leading to carrier landing error. Hence, taking F-18/A with ACLS as the research plane, a method to predict the carrier landing position based on BPNN with 3 discussed schemes, including landing error output, landing range output and dual parallel neural networks, was proposed in this paper. It was discovered through simulation in MATLAB that the proposed method was able to predict landing position with the mean error about 0.5m and the standard deviation about 2.60m. The prediction scheme with dual parallel neural networks performed better in such controlled circumstance.",
keywords = "BP neural network, air wake, carrier landing, deck heaving motion, prediction, real time",
author = "Chengxi Li and Gang Liu and Guanxin Hong",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 7th International Conference on Mechanical and Aerospace Engineering, ICMAE 2016 ; Conference date: 18-07-2016 Through 20-07-2016",
year = "2016",
month = aug,
day = "23",
doi = "10.1109/ICMAE.2016.7549593",
language = "英语",
series = "Proceedings of 2016 7th International Conference on Mechanical and Aerospace Engineering, ICMAE 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "507--511",
booktitle = "Proceedings of 2016 7th International Conference on Mechanical and Aerospace Engineering, ICMAE 2016",
address = "美国",
}