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. Particle swarm optimization is utilized to increase the training speed of neural network and avoid getting stuck in local extreme. 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. Simulation results verify that the proposed method can not only increase the training speed of neural network but also have 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.
關聯:
Journal of Control Engineering and Applied Informatics 16(4), p.73-80
