This research work explores the neural network learning capabilities by using a multi-layer perceptron artificial neural network to predict signal power loss by means of dataset from long term evolution network. The analysis of the effect of the learning rate parameter and the adoption of early stopping method during network training have been executed by using varied values of learning rate to ascertain the best learning rate during the neural network training. Also, there were neural network training without the application of learning rate and early stopping method and comparison have been made with the output results as shown in different tables. Output results comparisons have been performed using training regression and performance mean squared error. Two back propagation training algorithms, the Levenberg–Marquardt and the Bayesian Regularization algorithms were employed for the network training and comparison of their prediction abilities examined using same values of learning rates and on application of early stopping method as well as without learning rate and without early stopping method. The result shows an optimal performance of the neural network model on application of 0.005 learning rate and using 75%:15%:15% early stopping method with training regression 0.99267 and performance mean squared error 2.47 using Levenberg–Marquardt and training regression 0.99488 and performance mean squared error of 1.910 using Bayesian Regularization algorithms, respectively. Without application of learning rate and early stopping method, training the network using Levenberg–Marquardt algorithm gives training regression of 0.97111 and performance mean squared error of 7.38 using Levenberg–Marquart algorithm and training regression of 0.99248 and performance mean squared error of 4.42 using Bayesian Regularization algorithm. The margin between the two output results demonstrates the impact and importance of learning rate parameter as well as adopting early stopping method for neural network training for network optimization and better network generalization.

这项研究工作通过使用多层感知器人工神经网络通过长期演化网络的数据集来预测信号功率损耗，从而探索神经网络的学习能力。通过使用不同的学习率值来确定神经网络训练中的最佳学习率，已经进行了对学习率参数的影响的分析以及在网络训练中采用早期停止方法的方法。此外，还进行了不应用学习率和早期停止方法的神经网络训练，并与输出结果进行了比较，如下表所示。使用训练回归和性能均方误差进行了输出结果比较。两种反向传播训练算法，Levenberg-Marquardt和贝叶斯正则化算法用于网络训练，并使用相同的学习率值和早期停止方法以及没有学习率和没有早期停止方法来比较其预测能力。结果显示神经网络模型在应用0.005学习率和使用75％：15％：15％提前停止方法的情况下具有最佳性能，其中训练回归为0.99267，表现均方误差为2.47，使用Levenberg-Marquardt和训练回归为0.99488和表现使用贝叶斯正则化算法的均方误差为1.910。在没有应用学习率和早期停止方法的情况下，使用Levenberg-Marquardt算法训练网络可将训练回归设为0。使用Levenberg-Marquart算法获得97111，性能均方误差为7.38，使用贝叶斯正则化算法，训练回归为0.99248，性能均方误差为4.42。两次输出结果之间的余量表明了学习率参数的影响和重要性，并采用了神经网络训练的早期停止方法进行网络优化和更好的网络推广。