研究基于太空战场情况信息的低维多变量信息融合预测
Bo Wang1, Wen-Ya Zhou1, Jun Liu1
1School of Aeronautics and Astronautics, Dalian University of technology, Dalian 116024.
概括
本研究介绍了一种混合模型,将模糊认知地图 (FCM) 和回声状态网络 (ESN) 结合起来,用于准确地动态预测太空战场目标,增强实时决策.
科学领域:
- 人工智能的人工智能
- 太空系统工程 太空系统工程
- 防务技术 防务技术 技术
背景情况:
- 太空战场目标的动态预测对于有效的决策至关重要.
- 现有的方法难以应对太空战争场景的复杂性和实时需求.
- 准确的目标跟踪和预测对于战略优势至关重要.
研究的目的:
- 为动态太空战场目标开发一种新的混合预测模型.
- 在复杂的环境中提高目标预测的准确性和稳定性.
- 为增强实时太空战争决策支持提供基础.
主要方法:
- 将模糊认知地图 (FCM) 和回声状态网络 (ESN) 集成到混合模型中.
- 多变量时间序列数据的层次融合,以构建模糊的关系图.
- 使用遗传算法和偏差反机制优化节点关联.
主要成果:
- 与传统方法相比,拟议的混合FCM-ESN模型实现了明显较低的平均预测误差 (5.10%).
- 与遗传算法 (8.99%),粒子群优化 (10.39%) 和LSTM (63.78%) 相比,证明了更高的准确性.
- 在动态太空战场环境中验证了改进的预测稳定性.
结论:
- 混合FCM-ESN模型在太空战争的动态目标预测方面取得了重大进展.
- 该模型为实时太空战场决策提供了有效的支持.
- 源代码将发布,以促进同行评审和进一步研究.
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