人工神经网络微电池电场水平预测模型

A. Neskovic, N. Neskovic, D. Paunovic
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引用次数: 4

摘要

提出了一种新的移动电话环境微蜂窝预测模型。利用流行的前馈神经网络原理建立模型。利用新的人工神经网络(ANN)模型可以克服确定性模型和统计模型的一些重要缺点。为了建立该模型,在贝尔格莱德市对两个不同的测试发射机位置进行了广泛的电场电平测量(在900 MHz频段)。将所建立的电场电平预测模型与独立测量集的数据进行比较,结果表明所建立的模型具有较高的精度(在局部平均测量不确定度的量级上)和可靠性。同时,该算法简单、快速,适合计算机实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ANN microcell electric field level prediction model
A new microcell prediction model for the mobile telephone environment is presented. The principles of popular feedforward neural networks are used to build the model. Utilising a new artificial neural network (ANN) model some important disadvantages of both deterministic and statistical models can be overcome. In order to build the model, extensive electric field level measurements (in the 900 MHz frequency band) were carried out in the city of Belgrade, for two different test transmitter locations. The comparison between the data obtained by the proposed electric field level prediction model and the independent measurement sets have shown that the proposed model is accurate (on the order of the local mean measurement uncertainty) and reliable. At the same time, the algorithm is simple, fast and suitable for computer implementation.
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