利用非线性系统辨识预测VLF亚电离层波传播

H. Santosa, Y. Hobara
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引用次数: 0

摘要

甚低频(VLF)波已被用作监测和研究电离层下部(D/E区)的有力工具。本文利用外生输入非线性自回归神经网络模型(NARXNN)很好地表征了VLF信号传播的非线性物理过程。在此基础上,研究了利用NARXNN模型预测VLF传播波的日夜间平均振幅,以识别电离层沿大圆路径的扰动。NARXNN模型在预测时间序列数据和适合表示非线性模型的变化方面具有强大的功能。利用2014年3月15日至2016年5月26日各物理参数的日输入变量建立预测模型。所建立的模型对VLF电场振幅的逐日夜间预测具有较好的预测效果。NARXNN模型对不同纬度路径下的VLF振幅变化具有较好的预测效果。
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Prediction of VLF Sub-Ionospheric Wave Propagation Using Nonlinear System Identification
Very low frequency (VLF) waves have been used as a powerful tool to monitor and study the lower ionosphere (D/E region). In this paper, nonlinear physical processes of VLF signals propagation can be well represented by nonlinear autoregressive with exogenous input neural network (NARXNN) model. Further, a study of NARXNN model to predict the daily nighttime mean amplitude of VLF propagation wave to recognize the ionospheric perturbation along the great circle path. The NARXNN model is powerful in predicting time series data and suitable representations of a variation of nonlinear models. The daily input variables of various physical parameters with the time interval from 15 March 2014 to 26 May 2016 were used to build prediction model. The results of the built models are performing reasonably good for one-step ahead (OSA) predictions of the daily nighttime of VLF electric field amplitude. The NARXNN model has good performance for predicting the VLF amplitude variation for different latitude paths.
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