系统工程与电子技术

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基于BP神经网络的动能杆毁伤指标预测模型

王迎春, 王洁, 杜安利, 王锟   

  1. 空军工程大学防空反导学院, 陕西 西安 710051
  • 出版日期:2013-09-17 发布日期:2010-01-03

Damage indexes of KE-rod forecast model based on BP neural network

WANG Ying-chun, WANG Jie, DU An-li, WANG Kun   

  1. Institute of Air Defense & Antimissile, Air Force Engineering University, Xi’an 710051, China
  • Online:2013-09-17 Published:2010-01-03

摘要:

动能杆侵彻靶板的毁伤效果评估是一个复杂的问题。由于动能杆的数量多,侵彻碰撞情况多达上千种,计算费力又耗时。针对这些问〖JP+2〗题,提出了一种评价毁伤效果的数学模型,建立了基于反向传播(back propagation, BP)神经网络的预测模型,给出了隐含层节点数的计算方法,并在网络节点作用函数和随误差的变化动态更改参数方面对BP神经网络进行了改进。最后对仿真结果进行了分析,结果表明,75%的神经网络预测值分布在±10%误差范围之内,86%的神经网络预测值分布在±20%误差范围之内,验证了预测模型的可靠性和有效性。

Abstract:

The assessment of damage effect for kinetic energy rod (KE-rod) penetration on targets is a complex problem, and the computation is laborious and time consuming because of a large number of KE-rods and thousands of penetrations. Aiming at these problems, a mathematical model is put forward to evaluate the damage effect, the forecast model based on back propagation (BP) neural network is established, the calculative method of hidden layer nodes is given and the BP neural network is improved on the network function node and the dynamic changes of parameters with the error. The simulation results show that 75% of the predicted value based on neural network is distributed with ±10% error and 86% of the predicted value based on neural network is distributed with ±20% error, which validates that the forecast model is reliability and validity.