基于径向基函数神经网络的电离层法拉第极化旋转

Z. Stanković, M. Sarevska, N. Dončov
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引用次数: 1

摘要

本文提出了一种基于人工神经网络(ANN)的替代方法,用于估计在电离层中传播的电磁波的法拉第极化旋转。它使用一个由径向基函数神经网络开发的模型,根据接收终端的纬度和白天实例确定电磁波传播路径上自由电子总含量的近度值。然后用这个值来估计法拉第偏振旋转的角度。该模型提供了地中海地区夏季和冬季的角度估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Faraday Polarization Rotation in the Ionosphere using Radial Basis Function ANN
An alternative way, based on Artificial Neural Networks (ANN), for the estimation of Faraday polarization rotation of the electromagnetic wave that propagates in the ionosphere, is presented here. It uses a model developed by a Radial Basis Function ANN that determines approximate value of the total content of free electrons on the propagating path of the EM wave according to the latitude of the receiving terminal and daytime instance. Afterwards, this value is used to estimate the angle of Faraday Polarization Rotation. This model provides the estimation of the angle for summer and winter period in the Mediterranean area.
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