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Artificial Neural Network Optimal Modelling of Received Signal Strength in Mobile Communications Using UAV Measurements

机译:使用UAV测量的移动通信中接收信号强度的人工神经网络最优建模

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摘要

In this paper, we apply an alternative procedure for the prediction of received signal strength in mobile communications based on Artificial Neural Networks (ANN). We use experimental data measurements taken from an unmanned aerial vehicle (UAV) for ANN training. We apply several evolutionary algorithms (EAs) in conjunction with the Levenberg-Marquardt (LM) backpropagation algorithm in order to train different ANNs. We design two new hybrid training methods by combing LM with self-adaptive Differential Evolution (DE) strategies. These new training methods achieve better convergence of neural network weight optimization than the original LM method. The received results are compared to the real values using representative ANN performance indices and exhibit satisfactory accuracy.
机译:在本文中,我们应用了基于人工神经网络(ANN)的移动通信中接收信号强度的预测的替代过程。我们使用从无人机(UAV)的实验数据测量来进行ANN培训。我们将多个进化算法(EAS)与Levenberg-Marquardt(LM)BackProjagation算法一起应用,以培训不同的ANN。我们通过将LM与自适应差分进化(DE)策略梳理LM来设计两种新的混合培训方法。这些新的培训方法实现了神经网络权重优化的更好收敛于原始LM方法。使用代表性ANN性能指标的实际值进行比较,并表现出令人满意的精度。

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