Prediction of Projectile Muzzle Velocity Based on Neural Network Algorithm Combined with Clustering Association Rules

Ke TIAN

doi:10.12382/bgxb.2021.0687

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Prediction of Projectile Muzzle Velocity Based on Neural Network Algorithm Combined with Clustering Association Rules

更新时间：2025-08-18

- Prediction of Projectile Muzzle Velocity Based on Neural Network Algorithm Combined with Clustering Association Rules
- Acta Armamentarii Vol. 44, Issue 2, Pages: 452-461(2023)
- 作者机构：
  
  部队, 吉林白城 137001
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2021.0687
  CLC：
- Received：18 October 2021，
  
  Published Online：10 March 2023，
  
  Published：28 February 2023
- 稿件说明：
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Ke TIAN. Prediction of Projectile Muzzle Velocity Based on Neural Network Algorithm Combined with Clustering Association Rules[J]. Acta Armamentarii, 2023, 44(2): 452-461.
DOI：

Ke TIAN. Prediction of Projectile Muzzle Velocity Based on Neural Network Algorithm Combined with Clustering Association Rules[J]. Acta Armamentarii, 2023, 44(2): 452-461. DOI： 10.12382/bgxb.2021.0687.

摘要

针对靶场试验中利用初速雷达测试弹丸初速需要重构的情况

选择同时参试的两台雷达的数据进行融合建立神经网络模型

用一台雷达的数据预测出另一台雷达需要重构的数据。由于预测模型预测精度的高低取决于所建模型的好坏

而模型的好坏取决于样本数据的质量

先利用聚类分析和关联规则从大量历史试验数据中挖掘出优质的样本

再建立神经网络进行预测。实验结果表明

与支持向量回归机相比

由聚类分析关联规则神经网络构建的组合算法的预测精度更高

预测历史相似数据误差远小于1‰

预测与历史出入较大的数据的精度也较为理想。两种情况下的预测结果表明

组合算法既保证了预测精度又具有一定的鲁棒性

可以作为弹丸初速的预测模型。

Abstract

In view of the situation that reconstruction is needed for the muzzle velocity of a projectile measured by muzzle velocity radar in the range test

the data of two radars used in the test at the same time are fused to establish a neural network model

and the data of one radar is used to predict the data that needs to be reconstructed by the other radar. Because the prediction accuracy of the prediction model depends on the quality of the model

and the model quality depends on the quality of the sample data

we first use cluster analysis and association rules to mine high-quality samples from a large number of historical test data

and then establish a neural network for prediction. The experimental results show that: compared with support vector regression machine

the prediction accuracy of the combined algorithm constructed by clustering analysis association rules and neural network is higher

the error of predicting similar historical data is far less than 1‰

and the accuracy of predicting data significantly different from historical data is also more reasonable. The prediction results in the two cases show that the combined algorithm not only ensures the prediction accuracy

but also has certain robustness

and can be used as the prediction model of projectile muzzle velocity.

关键词

Keywords

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Thickness Equivalence Model Based on Serial Neural Network for Explosion Resistance of Radome

Rapid Prediction of Blast Loading in Dense Urban Building Complex Based on Neural Networks

Nonlinear Sliding Mode Control Based on Neural Network Compensation for Tank All-electric Bidirectional Stabilizers

A Novel Method of Stability Judgment and Milling Parameter Optimization Based on Cotes Integration Method and Neural Network

Related Author

Huachao DONG

Yichen JIANG

Jinglu LI

Peng WANG

Wenxin WANG

Hao ZHOU

Shuaiyin YAN

Hao CUI

Related Institution

School of Chemistry and Chemical Engineering, Nanjing University of Science and Technology

School of Mechanical Engineering, Nanjing University of Science and Technology

State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology

School of Intelligent System Engineering, Sun Yat-sen University

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