Application of Particle Swarm Optimization Based on Neural Network for Artillery Range Prediction

Abstract

The firepower of artillery is one of main factors to influence the war effectiveness. Traditionally, the army utilizes the firing table to modify the artillery range, but the fabrication of firing table of artillery costs a lot of time and ammunition. In this study, some firing data of artillery are utilized to train the back-propagation neural network for artillery range prediction and the training speed of neural network is increased by using particle swarm optimization. Besides, the orthogonal array is used to decrease the requirement of firing data and the proposed method is compared with the traditional back-propagation neural networks and regression analysis method. Simulation results verify that the proposed method can not only increase the training speed of neural network but also has the satisfied performance of range prediction, and the mean absolute percentage error can approach to 1.173%. The proposed method in this paper is usable for artillery range prediction and feasible for application in the army.