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作 者:唐卫贞[1] 刘波 黄洲升 田齐齐 TANG Weizhen;LIU Bo;HUANG Zhousheng;TIAN Qiqi(Civil Aviation Flight University of China,Deyang 618307,China)
出 处:《现代电子技术》2025年第8期127-132,共6页Modern Electronics Technique
摘 要:随着全球化进程的加快和航空技术的发展,对空中交通流量预测的精度要求也越来越高。为提高空中交通流量预测精度,减轻空中交通管制员的压力,提出一种增强位置编码的Transformer模型。利用小波变换对原始空域流量数据进行分析,通过信噪比选出性能最优的小波基函数,再进一步计算出小波系数并将其融入位置编码,以增强模型对时间序列数据的理解能力。实验结果表明,所提模型能够准确捕捉空中交通流量数据中的非平稳性和突变特征,其RMSE和MAPE评估指标较原始Transformer模型分别降低了29.9与2.9%,较LSTM模型分别降低了34.5与3.4%。该模型不仅提升了空域流量预测的准确性,也证实了小波变换在增强模型时间序列数据理解中的有效性,且为交通流量管理提供了一种新的技术方案。With the acceleration of globalization and the development of aviation technology,the accuracy requirements for air traffic flow prediction are also increasing.In order to improve the accuracy of air traffic flow prediction and reduce the pressure of air traffic controllers,a Transformer model with enhanced position coding is proposed.It uses wavelet transform to analyze the original airspace flow data,selects the wavelet basis function with the best performance by means of the signal-to-noise ratio,and further calculates the wavelet coefficient and integrates it into the position coding,so as to enhance the model's understanding ability of time series data.The experimental results show that the proposed model can accurately capture the non-stationary and abrupt characteristics of air traffic flow data.Its RMSE and MAPE evaluation indicators are decreased by 29.9 and 2.9%respectively compared with the original Transformer model,and by 34.5 and 3.4%compared with the long short-term memory(LSTM)model,respectively.This model not only improves the accuracy of airspace flow prediction,but also confirms the effectiveness of wavelet transform in enhancing the understanding of model time series data,and provides a new technical scheme for traffic flow management.
关 键 词:空域流量预测 增强位置编码 Transformer模型 小波变换 LSTM模型 小波基函数
分 类 号:TN919.3-34[电子电信—通信与信息系统]
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