Time series modeling and prediction of wideband electric field radiation

Xinwei Song, Yijie Chen
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引用次数: 1

Abstract

Exposure to wideband electromagnetic radiation (EMR) over seven days was measured in this paper, and a short-time Fourier transform analysis on this showed that the change of EMR over time conforms to people’s daily work and rest cycle and is affected by the intensive use of wireless communication devices, thus showing strong low-frequency periodicity and high-frequency fluctuations. These characteristics lead to a significant decrease in the performance of traditional EMR time series mod-els. To this end, a hybrid model based on wavelet decomposition (WD) and long-short time memory (LSTM) is proposed for the wideband electric field radiation prediction. The proposed model is verified by the measurement samples under different prediction steps. It is also compared with other representative models, and the results show that the proposed model outperforms other models in prediction accuracy and prediction steps.
宽带电场辐射的时间序列建模与预测
本文测量了7天内的宽带电磁辐射(EMR)暴露,并对其进行短时傅里叶变换分析,发现EMR随时间的变化符合人们日常的作息周期,受无线通信设备密集使用的影响,呈现出较强的低频周期性和高频波动。这些特征导致传统EMR时间序列模型的性能显著下降。为此,提出了一种基于小波分解(WD)和长短时记忆(LSTM)的宽带电场辐射预测混合模型。通过不同预测步骤下的实测样本对模型进行了验证。并与其他代表性模型进行了比较,结果表明该模型在预测精度和预测步骤上都优于其他模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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