Projectile parameter identification: extreme learning machine optimized by improved particle swarm

被引：0

作者：

Xia Y. ^{[1
]}

Guan J. ^{[1
,2
]}

Yi W. ^{[1
]}

机构：

[1] National Key Lab of Transient Physics, Nanjing University of Science and Technology, Nanjing

[2] School of Electronic and Information, Jiangsu University of Science and Technology, Zhenjiang

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 02期

关键词：

adaptive update strategy; aerodynamic parameter identification; extreme learning machine; particle mutation strategy; particle swarm optimization algorithm; projectile;

D O I：

10.12305/j.issn.1001-506X.2023.02.24

中图分类号：

学科分类号：

摘要：

In view of the identification results diverge when using extreme learning machine to identify the aerodynamic parameters of the projectile, due to the randomly generated input weights and hidden layer neuron thresholds, an adaptive mutation particle swarm optimization extreme learning machine algorithm is proposed. The paper introduces the adaptive update strategy and particle mutation strategy into particle swarm optimization algorithm and couples it with extreme learning machine. The proposed algorithm optimizes the input weights and hidden layer thresholds of extreme learning machine through adaptive mutation particle swarm optimization algorithm, the adaptive update strategy and particle mutation strategy in algorithm effectively improve the performance of the algorithm. Simulation experiments show that the use of adaptive mutation particle swarm optimization extreme learning machine exceedingly improve identification accuracy and convergence speed, which is practical in engineer application. © 2023 Chinese Institute of Electronics. All rights reserved.

引用

页码：521 / 529

页数：8

共 43 条

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