基于神经网络的移动通信网络异常信号识别优化
Optimization of Abnormal Signal Recognition in Mobile Communication Net-works Based on Neural Networks
廉咪咪 1刘洋1
作者信息
- 1. 郑州工商学院信息工程学院,河南郑州 450008
- 折叠
摘要
常规的移动通信网络异常信号识别是在理想环境下进行的,实际的异常信号识别往往受到其他干扰的影响,出现识别误差的问题.因此,设计了基于神经网络的移动通信网络异常信号识别优化方法.提取移动通信网络异常信号特征,将短时能量信号与过零信号的时域提取出来,过滤时域上的噪声信号,保留异常信号存在的部分.基于神经网络构建通信网络异常信号识别模型,将异常信号特征神经元作为输入,加权求和输入神经元特征,以激活阈值判断当前信号是否异常,从而优化异常信号识别精准度.优化网络异常信号识别的回归损失,降低模型训练损失,从而符合模型输出预期.采用对比实验,验证了该方法的识别准确性更高,优化效果更佳,能够应用于实际生活中.
Abstract
Conventional mobile communication network abnormal signal recognition is carried out in an ideal environment,but actual abnormal signal recognition is often affected by other in-terferences,resulting in recognition errors.Therefore,an optimization method for identifying abnormal signals in mobile communication networks based on neural networks was designed.Extract the characteristics of abnormal signals in mobile communication networks,extract the time-domain of short-term energy signals and zero crossing signals,filter the noise signals in the time-domain,and retain the parts of abnormal signals.A communication network abnormal sig-nal recognition model is constructed based on neural networks.The abnormal signal feature neu-rons are used as inputs,and the weighted sum of the input neuron features is used to activate the threshold to determine whether the current signal is abnormal,thereby optimizing the accuracy of abnormal signal recognition.Optimize the regression loss of network abnormal signal recog-nition,reduce model training loss,and thus meet the expected output of the model.Through comparative experiments,it was verified that this method has higher recognition accuracy and better optimization effect,and can be applied in practical life.
关键词
神经网络/移动通信/异常信号识别Key words
neural network/Mobile communication/Abnormal signal recognition引用本文复制引用
出版年
2024
NETL
NSTL
万方数据