文章摘要
基于GA和ARMA模型的制导弹药技术状态预测
Technical Conditions Prognostics for Guided Munition Based on Genetic Algorithm and ARMA Model
投稿时间:2019-06-03  修订日期:2019-07-09
DOI:
中文关键词: 遗传算法  ARMA模型  制导弹药  技术状态
英文关键词: genetic algorithm, ARMA model, the guided munition, technical conditions
基金项目:
作者单位E-mail
张毅 空军勤务学院 zhyixs@163.com 
马长刚 93956部队  
张国豪 94303部队  
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中文摘要:
      针对定期检测判断制导弹药技术状态影响弹药寿命和实时性较差的问题,提出了基于遗传算法(GA)优化的自回归与移动平均(ARMA)模型制导弹药技术状态预测方法。在获取到制导弹药状态特征量历史检测数据的基础上,采用遗传算法对ARMA模型的阶数进行优化,通过实例将遗传算法优化后的ARMA模型与经典ARMA模型的预测结果进行比较分析,结果表明经过优化后的ARMA模型具有更好的预测效果。该方法提高了制导弹药技术状态确定的实时性,对降低、消除制导弹药故障危害具有实际意义。
英文摘要:
      Aiming at the problem of periodic inspections which will affect the life of the ammunition when judging the technical conditions of the guided munition and have poor real-time performance, prediction method of guided munition technical conditions based on autoregressive and moving average (ARMA)model optimized by genetic algorithm (GA) is proposed. Based on the historical data of the state characteristic quantity of the guided munition, the genetic algorithm is used to optimize the order number of the ARMA model. The prediction results of ARMA model Optimized by genetic algorithm and classic ARMA model are comparatively analyzed by an example. The results show that the optimized ARMA model has a better prediction effect. The method improves the real-time determination of the technical conditions of the guided munition, and has practical significance for reducing and eliminating the damage of the munition.
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