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

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.