利用BP神经网络算法提高ZigBee的距离预测精度

Zhang Xuhui, J. Junfeng, Wei Jiaxin, Zhao Yingjie
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引用次数: 2

摘要

本文提出利用BP神经网络算法提高ZigBee之间距离的准确性。首先,对无线信令路径损耗模型、基于RSSI的距离测量原理以及BP神经网络算法进行了全面分析。其次,从ZigBee硬件平台获取实验数据。最后,利用BP神经网络算法对实验数据进行了仿真和分析。仿真分析结果表明,120m范围内距离预测的最大误差为7.62%,预测精度显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using BP Neural Network Algorithm Improve the Prediction Accuracy of Distance between ZigBee
This paper proposes that we can improve the accuracy of the distance between ZigBee with the algorithm of BP neural network. Firstly, we analyze the wireless signaling path loss model, the principle of measuring distance based on RSSI, and the algorithm of BP neural network fully. Secondly, we get the experimental data from the hardware platform of ZigBee. Finally, we use the algorithm of BP neural network to emulate and analyze the experimental data. Based on the results of the emulation and analysis, the maximum error of the distance prediction within 120m is 7.62%, with the prediction accuracy improved significantly.
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