AdaBoost-PSO-LSTM网络实时预测机动轨迹

V247.5; 针对自主空战中轨迹预测难以同时保持高预测精度和短预测时间的问题,提出一种自适应增强的粒子群优化长短期记忆网络预测方法.首先,建立三自由度无人机动力学模型,解决机动轨迹的数据来源问题.其次,分析长短期记忆网络,并引入在线预测的滑动模块输入矩阵,利用粒子群优化算法代替传统基于时间的反向传播算法进行网络内部权值更新;同时为解决优化算法非定向性问题,提出数据共享方法.然后,为进一步提高预测精度,采用自适应增强算法搭建外框架,通过控制弱预测器的数量平衡预测精度与预测时间.最后,在一段变化较为频繁的轨迹进行预测,与5种神经网络预测方法进行比较,结果表明所提方法能够较好地满足精度和时间要求....

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Published in系统工程与电子技术 Vol. 43; no. 6; pp. 1651 - 1658
Main Authors 谢磊, 丁达理, 魏政磊, 汤安迪, 张鹏
Format Journal Article
LanguageChinese
Published 空军工程大学航空工程学院,陕西西安710038 01.06.2021
Subjects
无人机
粒子群优化长短期记忆网络
动力学模型
轨迹预测
Online AccessGet full text
ISSN1001-506X
DOI10.12305/j.issn.1001-506X.2021.06.23

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Abstract V247.5; 针对自主空战中轨迹预测难以同时保持高预测精度和短预测时间的问题,提出一种自适应增强的粒子群优化长短期记忆网络预测方法.首先,建立三自由度无人机动力学模型,解决机动轨迹的数据来源问题.其次,分析长短期记忆网络,并引入在线预测的滑动模块输入矩阵,利用粒子群优化算法代替传统基于时间的反向传播算法进行网络内部权值更新;同时为解决优化算法非定向性问题,提出数据共享方法.然后,为进一步提高预测精度,采用自适应增强算法搭建外框架,通过控制弱预测器的数量平衡预测精度与预测时间.最后,在一段变化较为频繁的轨迹进行预测,与5种神经网络预测方法进行比较,结果表明所提方法能够较好地满足精度和时间要求.
AbstractList V247.5; 针对自主空战中轨迹预测难以同时保持高预测精度和短预测时间的问题,提出一种自适应增强的粒子群优化长短期记忆网络预测方法.首先,建立三自由度无人机动力学模型,解决机动轨迹的数据来源问题.其次,分析长短期记忆网络,并引入在线预测的滑动模块输入矩阵,利用粒子群优化算法代替传统基于时间的反向传播算法进行网络内部权值更新;同时为解决优化算法非定向性问题,提出数据共享方法.然后,为进一步提高预测精度,采用自适应增强算法搭建外框架,通过控制弱预测器的数量平衡预测精度与预测时间.最后,在一段变化较为频繁的轨迹进行预测,与5种神经网络预测方法进行比较,结果表明所提方法能够较好地满足精度和时间要求.
Author 谢磊
魏政磊
张鹏
汤安迪
丁达理
AuthorAffiliation 空军工程大学航空工程学院,陕西西安710038
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WEI Zhenglei
TANG Andi
XIE Lei
ZHANG Peng
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粒子群优化长短期记忆网络
动力学模型
轨迹预测
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Snippet V247.5; 针对自主空战中轨迹预测难以同时保持高预测精度和短预测时间的问题,提出一种自适应增强的粒子群优化长短期记忆网络预测方法.首先,建立三自由度无人机动力学模型,...
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Title AdaBoost-PSO-LSTM网络实时预测机动轨迹
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