@inproceedings{7d61fbcdf7b14f069016861b6880337e,
title = "Rapid simulated annealing algorithm for optimization of aeroengine control based on BP neural network",
abstract = "In this paper, the BP neural network is used to adjust the PID controller of the turboshaft engine, and aiming at its disadvantage of being easy to fall into local minimum point, a rapid simulated annealing algorithm is proposed to optimize the BP neural network by learning and adjusting its weights. The simulations, under different flight altitudes and flight Mach number, show that the optimization algorithm improves its shortcoming, the convergence speed of the BP neural network algorithm, and the working efficiency of the aeroengine.",
keywords = "Aeroengine, BP neural network, PID controller, Rapid simulated annealing algorithm",
author = "Linfeng Gou and Wenxin Shao and Xianyi Zeng and Yawen Shen and Zihan Zhou",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8866588",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "8848--8852",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
}