Study on the Prediction of Launcher Efficiency of Electromagnetic Launcher Based on Particle Swarm Optimization-Improved BP Neural Network

被引：0
|
作者：
Xiao, Nan [1 ,2 ]
Li, Jun [3 ]
Yan, Ping [1 ,2 ]
机构：
[1] Chinese Acad Sci, Inst Elect Engn, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
[3] Beijing Inst Special Electromech Technol, Beijing 100012, Peoples R China
来源：
ENERGIES | 2024年 / 17卷 / 18期
关键词：
particle swarm optimization; BP neural network; electromagnetic launcher; launcher efficiency; BORE;
D O I：
10.3390/en17184547
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Launcher efficiency is an important index for evaluating the performance of the electromagnetic launcher, and it reflects the ability of the launcher to convert input electrical energy into kinetic energy of the armature. In this paper, the launcher efficiency is taken as the objective function of bore parameter optimization, and particle swarm optimization is used to optimize the initial parameters of the BP neural network to improve the accuracy of the neural network in predicting launcher efficiency. The results show the following: (1) The predicted efficiency of the launcher shows the same trend as the experimental results. When the ratio of rail separation and rail height is greater than 1.75, the rate of change in the launcher efficiency curve decreases as the rail separation increases. (2) The weight of the influence of each parameter on the launcher efficiency follows the following law: convex arc height > rail separation > rail height > rail thickness. (3) The mean absolute error of the BP neural network prediction is 0.70%; after optimization by PSO, the mean absolute error is reduced to 0.28% and the mean relative accuracy is improved from 0.9774 to 0.9910, which indicates the feasibility of the PSO-BP neural network for the prediction of the launcher efficiency of an electromagnetic launcher.
引用
收藏
页数：14
相关论文
共 50 条
  • [31] Rock burst risk evaluation based on particle swarm optimization and BP neural network
    Li, H. (cqlihm@163.com), 1600, China University of Mining and Technology (31):
  • [32] The Research on BP Neural Network Model Based on Guaranteed Convergence Particle Swarm Optimization
    Tang, Pingzhou
    Xi, Zhaocai
    2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL II, PROCEEDINGS, 2008, : 13 - +
  • [33] Study on the Prediction of Haematococcus pluvialis Population based on Particle Swarm Optimization Neural Network Algorithm
    Cui Shigang
    Li Yunfei
    Zhang Yongli
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 70 - 73
  • [34] Prediction for network traffic of radial basis function neural network model based on improved particle swarm optimization algorithm
    Zhang, Weijie
    Wei, Dengfeng
    NEURAL COMPUTING & APPLICATIONS, 2018, 29 (04): : 1143 - 1152
  • [35] Prediction for network traffic of radial basis function neural network model based on improved particle swarm optimization algorithm
    Weijie Zhang
    Dengfeng Wei
    Neural Computing and Applications, 2018, 29 : 1143 - 1152
  • [36] An Improved PID Neural Network Control Algorithm Based on Particle Swarm Optimization
    Dou, Chunhong
    Zhang, Ling
    2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL VI, 2010, : 32 - 34
  • [37] Emission Method of Tractor Based on Improved Particle Swarm Optimization Neural Network
    Zheng B.
    Deng Z.
    Luo Z.
    Song Z.
    Mao E.
    Yang Z.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2023, 54 : 417 - 426
  • [38] An Improved PID Neural Network Control Algorithm Based on Particle Swarm Optimization
    Dou, Chunhong
    Zhang, Ling
    2011 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION AND INDUSTRIAL APPLICATION (ICIA2011), VOL I, 2011, : 32 - 34
  • [39] Neural network based on neighborhood topology improved particle swarm optimization algorithm
    Hua, Jingxin
    Chen, Zhimin
    Bo, Yuming
    Zhang, Jie
    Zhu, Jianliang
    Journal of Information and Computational Science, 2013, 10 (02): : 587 - 597
  • [40] Soft sensor for ammonia concentration at the ammonia converter outlet based on an improved particle swarm optimization and BP neural network
    Xu, Wei
    Zhang, Lingbo
    Gu, Xingsheng
    CHEMICAL ENGINEERING RESEARCH & DESIGN, 2011, 89 (10A): : 2102 - 2109
12345