| 鹿瑶,张建军,傅耘,刘聪.基于Elman网络的导引头舱停放温度环境条件预计[J].装备环境工程,2020,17(7):21-26. LU Yao,ZHANG Jian-jun,FU Yun,LIU Cong.Seeker Cabin Temperature Prediction Based on Elman Neural Network in Airport Parking Conditions[J].Equipment Environmental Engineering,2020,17(7):21-26. |
| 基于Elman网络的导引头舱停放温度环境条件预计 |
| Seeker Cabin Temperature Prediction Based on Elman Neural Network in Airport Parking Conditions |
| 投稿时间：2020-05-29 修订日期：2020-06-20 |
| DOI：10.7643/issn.1672-9242.2020.07.005 |
| 中文关键词: 停放温度环境条件预计 Elman网络 导弹导引头舱 |
| 英文关键词:parking temperature conditions prediction elman network missile seeker cabin |
| 基金项目: |
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| Author | Institution |
| LU Yao | China Aero-Polytechnology Establishment, Beijing 100028, China;Key Laboratory of Quality Infrastructure Efficacy Research, AQSIQ, Beijing 100028, China |
| ZHANG Jian-jun | China Aero-Polytechnology Establishment, Beijing 100028, China;Key Laboratory of Quality Infrastructure Efficacy Research, AQSIQ, Beijing 100028, China |
| FU Yun | China Aero-Polytechnology Establishment, Beijing 100028, China;Key Laboratory of Quality Infrastructure Efficacy Research, AQSIQ, Beijing 100028, China |
| LIU Cong | Southwest Technology and Engineering Research Institute, Chongqing 400039, China |
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| 摘要点击次数: |
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| 中文摘要: |
| 目的 克服现场温度测试难以短时间内获得高温极值的缺陷，准确制定导弹导引头舱机场停放高温环境条件，提出一种导引头舱内高温预计的方法。方法 在导引头舱内温度测试数据基础上，建立基于Elman网络的导引头舱温度预计模型，并与BP网络预计模型、线性网络预计模型进行对比，通过均方误差(MSE)、拟合相对误差(MRE)和最大绝对误差(MAE)等指标评估3种模型的预计能力。结果 基于Elman网络的温度预计模型精度比BP网络高出约1 ℃，比线性网络高出约1.5 ℃。结论 Elman网络温度预计模型具备准确预计导引头舱内温度的能力，该方法可用于导弹停放温度预计工作，为确定导弹贮存温度环境适应性要求提供参考。 |
| 英文摘要: |
| The paper aims to propose a method for predicting the high temperature in the seeker cabin to solve the problem that it is difficult to obtain the high temperature extreme value in a short time in the site temperature measurement and customize the high temperature environment conditions for airplane parking of missile seeker cabin. An Elman network model for temperature prediction was established based on seeker cabin measured data. Compared with BP network model, linear network model, the prediction capacity of the three models were evaluated in terms of mean square error (MSE), mean relative error (MRE), maximum absolute error (MAE) and other indicators. The precision oftemperature prediction model based on Elman network was about 1 ℃ higher than that of BP network, and was about 1.5 ℃ higher than that of linear network.Elman network temperature prediction model has the ability to accurately predict the temperature in the seeker cabin. This method can be used to predict the parking temperature of missiles. It provides reference for determining the environmental adaptability conditions of missile storage temperature. |
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