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使用先进的人工神经网络模型预测射弹残速.

Afsar Husain1, Mohd Danish2, Sanan H Khan1

  • 1Department of Mechanical and Aerospace engineering, United Arab Emirates University, Al-Ain, Abu Dhabi, 15551, United Arab Emirates.

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一个人工神经网络 (ANN) 模型准确地预测了弹子的剩余速度,超过了传统方法. 这项创新有助于设计先进的防护屏障和装甲系统.

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吸收的能量 吸收的能量人工神经网络 (ANN) 是一个人工神经网络.项目影响 项目影响保护性障碍 保护性障碍 保护性障碍在Recht-Ipson模型中,剩余的速度是剩余的速度

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科学领域:

  • 弹道学和撞击力学
  • 计算机建模和模拟

背景情况:

  • 剩余速度对于设计防护屏障至关重要.
  • 测量残速的传统方法往往是低效和不一致的.

研究的目的:

  • 开发和验证人工神经网络 (ANN) 模型,用于预测射弹的剩余速度.
  • 将ANN模型的准确性与传统方法 (如Recht-Ipson模型) 的准确性进行比较.

主要方法:

  • 使用MATLAB R2021a.人工神经网络 (ANN) 模型的开发.
  • 在数据集上训练ANN模型,包括初始投射速度,材料,形状和目标厚度.
  • 验证ANN模型的预测性能.

主要成果:

  • 在训练和验证过程中,ANN模型以低的平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE) 证明了卓越的准确性.
  • 在准确性方面,ANN模型显著超过了Recht-Ipson模型.
  • 该模型显示了预测吸收能量的潜力.

结论:

  • 开发的ANN模型提供了一种更准确,更有效的方法来预测弹子的剩余速度.
  • 这种方法对保护结构和装甲系统的设计有重大影响.
  • 该ANN模型的多功能性扩展到预测吸收的能量,提高其适用性.