基于尺度共轭梯度神经网络的室内定位系统优化

N. Aburaed, Shadi Atalla, Husameldin Mukhtar, M. Al-Saad, W. Mansoor
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引用次数: 6

摘要

本文对几种室内定位系统进行了综述,提出了一种基于缩放共轭梯度(SCG)算法的深度神经网络(DNN)算法。在提出的室内定位系统中,以接收信号强度(RSS)作为指纹来识别建筑物和楼层的室内位置。对系统的性能进行评估,并与其他基于机器学习的定位系统进行比较。当使用标准数据集进行测试时,所提出的深度神经网络的准确率为99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scaled Conjugate Gradient Neural Network for Optimizing Indoor Positioning System
In this paper, several indoor positioning systems are reviewed and a deep neural network (DNN) algorithm based on Scaled Conjugate Gradient (SCG) algorithm is proposed. In the proposed indoor positioning system, Received Signal Strength (RSS) is used as a fingerprint to identify the indoor location in terms of Building and Floor. The performance of the system is evaluated and compared against other machine learning based positioning systems. The accuracy of the proposed DNN is 99% when tested using a standard dataset.
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