@inproceedings{9c0f60756b9148f9b1dac64b729dd1ba,
title = "Case study of aeroengine parameter prediction based on MIV and ELM",
abstract = "Aiming at the problems existing in the current prediction methods of aeroengine parameters, such as the difficulty in parameter selection, the slow training speed and the tendency to fall into local optimal solution of traditional BP neural network algorithm, this paper proposes the prediction method of aeroengine performance parameters based on mean influence value (MIV) algorithm and extreme learning machine (ELM). Firstly, we preprocess the sample data. Secondly, screening out the main parameters that affect the predicted parameters by MIV algorithm, attribute reduction is realized, the result of attribute reduction is taken as the input to train an ELM. Finally, using the test samples to do the test. The testing results show that the algorithm is faster and more accurate in parameter prediction.",
keywords = "Aeroengine, ELM, MIV",
author = "Yingshun Li and Fuyang Wang and Ximing Sun and Xiaojian Yi",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 ; Conference date: 15-08-2019 Through 17-08-2019",
year = "2019",
month = aug,
doi = "10.1109/SDPC.2019.00019",
language = "English",
series = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "56--61",
editor = "Chuan Li and Shaohui Zhang and Jianyu Long and Diego Cabrera and Ping Ding",
booktitle = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
address = "United States",
}