基于WOA-LSTM的窄带通信网网络时延预测算法  

Network delay prediction algorithm based on WOA-LSTM for narrowband communication networks

作　　者：苏鹏飞 徐松毅[1] 于晓磊[1] SU Pengfei;XU Songyi;YU Xiaolei(The 54th Research Institute of CETC,Shijiazhuang,Hebei 050081,China)

机构地区：[1]中国电子科技集团公司第五十四研究所,河北石家庄050081

出　　处：《河北工业科技》2022年第1期9-15,共7页Hebei Journal of Industrial Science and Technology

摘　　要：为了给窄带通信网的链路选择及协议的智能切换提供实时参考,设计了一种基于鲸鱼优化算法(WOA)和长短期记忆神经网络(LSTM)的窄带通信网网络时延预测算法。首先对实测数据样本进行标准化处理,以LSTM神经网络算法的均方根误差函数的倒数作为适应度函数;其次采用鲸鱼优化算法对LSTM神经网络的学习率、隐含层神经元个数进行优化,最后将全局最优解输出作为LSTM神经网络的初始参数对样本进行训练预测。结果表明,基于WOA-LSTM的网络时延预测算法预测精度相较于LSTM神经网络算法和BP神经网络算法分别提高了14.87%和78.89%,WOA-LSTM达到收敛时迭代次数相较于LSTM神经网络算法减少了11.11%。所提算法新颖可靠,可更准确地进行网络时延预测,为窄带通信网网络的智能化与自动化升级提供数据支持。In order to provide real-time reference for link selection and protocol intelligent switching in narrowband communication networks,a network delay prediction algorithm based on whale optimization algorithm(WOA)and long short-term memory(LSTM)was designed.Firstly,the measured data samples were standardized,and the reciprocal of root mean square error function of LSTM neural network algorithm was used as fitness function.Secondly,the whale optimization algorithm was used to optimize the learning rate and the number of hidden layer neurons of LSTM neural network.Finally,the output of global optimal solution was used as the initial parameter of LSTM neural network to train and predict samples.The results show that compared with LSTM neural network algorithm and BP neural network algorithm,the prediction accuracies of network delay prediction algorithm based on WOA-LSTM are improved by 14.87%and 78.89%respectively,and the iteration times of WOA-LSTM are reduced by 11.11%compared with LSTM neural network algorithm when WOA-LSTM reaches convergence.The algorithm is novel and reliable,which can predict network delay more accurately and provide data support for intelligent and automatic upgrade of narrowband communication networks.

关 键 词：计算机神经网络 鲸鱼优化算法 LSTM神经网络 窄带通信网 网络时延预测 

分 类 号：TN915.1[电子电信—通信与信息系统]