Study on Optimization of Final Trajectory Parameters of Blasting Warheads Based on Extreme Learning Machine

Abstract

In order to study the complex interaction of the final trajectory parameters and the lethal area to optimize the final trajectory parameters, the fragment dispersion process was analyzed, and the fragment dispersion model was established to calculate the effective fragments on the ground. The lethal area of the warhead under different final trajectory parameters was solved by counting the number of effective fragments on the ground. The lethal area under 1 225 final trajectory combinations was obtained. On this basis, the extreme learning machine(ELM)was used to optimize the calculation of 1 903 993 final trajectory combinations. The results show that the determination coefficient can reach more than 0.9, and the solution time is much less than 1 s, and the lethal area increases from 438.1 m2 to 541.2 m2 when the number of hidden nodes is more than 200 and the sigmoid function is used to the extreme learning machine.