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Feasibility of neural networks in modelling radio propagation for field strength prediction

机译:神经网络在模拟无线电传播以进行场强预测中的可行性

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摘要

A typical back-propagation neural network (BPN) model is developed for modelling radio propagation for field strength prediction based on data measurements of propagation loss (in decibels) with terrain information taken in an urban area (Athens region) in the 900 MHz band. The feasibility of the BPN model is checked against the performance of a conventional semiempirical reference model. The performance of both models is quantified by statistical methods. The evaluation is done by comparing their prediction error statistics of average absolute, standard deviation and root mean square and by comparing their percentage accuracy and correlation of predicted values relative to true data measurements. © 1998 John Wiley & Sons, Ltd.
机译:开发了一种典型的反向传播神经网络(BPN)模型,用于基于传播损耗(以分贝为单位)的数据测量以及在900 MHz频带中市区(雅典地区)拍摄的地形信息,对用于场强预测的无线电传播建模。针对传统的半经验参考模型的性能,检查了BPN模型的可行性。两种模型的性能均通过统计方法进行量化。通过比较它们的平均绝对值,标准偏差和均方根的预测误差统计数据,并比较它们的百分比准确性和相对于真实数据测量值的预测值的相关性,来进行评估。 ©1998 John Wiley&Sons,Ltd.

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