Coupled extreme learning machine and particle swarm optimization variant for projectile aerodynamic identification

Abstract

Accurate aerodynamic parameters are the basis for the research of uncontrolled projectile drop point dispersion and precise strike. The traditional methods for aerodynamic parameters rely strongly on the projectile dynamics system. To weaken the influence of kinetic effects, an extreme learning machine (ELM) optimized by particle swarm optimization (PSO) is applied. However, the iterative optimization process of PSO makes the algorithm more complicated and impairs the real-time performance of ELM. To accelerate the convergence of PSO, a new hybrid optimization strategy is proposed. The hybrid optimization strategy combines the advantages of the chaos optimization strategy, the adaptive update strategy, and the mutation strategy. The chaos optimization strategy optimizes the distribution of the initial swarm to improve the optimization efficiency of PSO. The adaptive update strategy tunes the velocity inertia weight based on the value of the evolutionary factor to match the current searching state of the particles. The mutation strategy mutates the particles to break out of the local convergence. Numerical experiments show that the introduction of the hybrid optimization strategy enables ELM to exhibit excellent robustness, real-time performance, and accuracy in noisy environments. The hybrid algorithm has excellent prospects for application and extension for the parameter identification of dynamic systems in complex environments.