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Journal of Projectiles, Rockets, Missiles and Guidance >

2025 , Vol. 45 >Issue 6: 1074 - 1081

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.06.015

Academic article

A Study on the Modeling Method for Rapid Prediction of Shooting Density Based on Multi-Layer Perceptron Neural Network

  • HAN Mengfan , 1 ,
  • QIU Ming , 1, * ,
  • SONG Jie 1 ,
  • LU Dabin 2 ,
  • XU Xiao 2
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  • 1 School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
  • 2 No.208 Research Institute of China Ordnance Industries, Beijing 102202, China

Received date: 2024-10-17

  Online published: 2026-01-24

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Abstract

To improve the prediction efficiency of the impact point of small-caliber bullets, this paper proposes a rapid prediction method for the bullet impact point based on the Multilayer Perceptron (MLP). The method achieves an efficient mapping from the initial conditions of the projectile to the muzzle attitude of the projectile through the MLP, and combines a six-degree-of-freedom external ballistic model to calculate the projectile’s landing position at 300 meters. The MLP is trained with a dataset from finite element calculations of the projectile’s muzzle attitude, and compares the calculation accuracy and efficiency of the MLP-based method with the traditional finite element calculation method. The results show that the prediction results based on the MLP are essentially consistent with the calculation results of the finite element model, but the calculation time is significantly reduced. Under the same hardware configuration, the MLP model reduces the calculation time from 673.1 minutes of the finite element model to 9 minutes. This method significantly improves the calculation efficiency while ensuring accuracy, providing a new approach for rapidly predicting ballistic accuracy and improving the utilization of computing resources.

Key words： Multilayer Perceptron; Ballistic Prediction; Initial Disturbance; Rapid Computation; Shooting Accuracy

Cite this article

HAN Mengfan , QIU Ming , SONG Jie , LU Dabin , XU Xiao . A Study on the Modeling Method for Rapid Prediction of Shooting Density Based on Multi-Layer Perceptron Neural Network[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2025 , 45(6) : 1074 -1081 . DOI: 10.15892/j.cnki.djzdxb.2025.06.015

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