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

Nan Xiao, Jun Li () and Ping Yan
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Nan Xiao: Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
Jun Li: Beijing Institute of Special Electromechanical Technology, Beijing 100012, China
Ping Yan: Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China

Energies, 2024, vol. 17, issue 18, 1-14

Abstract: 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.

Keywords: particle swarm optimization; BP neural network; electromagnetic launcher; launcher efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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