A new method for spinning projectile aerodynamic estimation: Extreme learning machine optimized by adaptive particle swarm

• Published in: AIP advances Vol. 11; no. 12; pp. 125102 - 125102-9
• Main Authors: Guan, Jun, Yi, Wenjun, Xia, Youran
• Format: Journal Article
• Language: English
• Published: Melville American Institute of Physics 01.12.2021; AIP Publishing LLC
• Subjects: Adaptive algorithms; Algorithms; Artificial neural networks; Computer simulation; Free flight; Machine learning; Parameter identification; Particle swarm optimization; Projectiles
• ISSN: 2158-3226; 2158-3226
• DOI: 10.1063/5.0076103

Aiming at the problem of aerodynamic parameter identification of a spinning projectile, an adaptive particle swarm optimization for the extreme learning machine algorithm is proposed in this paper. The algorithm uses the adaptive particle swarm optimization algorithm to optimize the hidden layer weight and threshold of the extreme learning machine to avoid the problem of unstable identification results caused by the random weight and threshold of the traditional extreme learning machine. The free flight data of the projectile are generated by numerical simulation, and the aerodynamic parameters of a projectile are identified by the proposed algorithm. Simulation results show that the proposed algorithm can effectively identify the aerodynamic parameters of the projectile, and it has high identification accuracy and fast convergence speed. The proposed algorithm is suitable for engineering applications.