基于WOA-LSTM的窄带通信网网络时延预测算法
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作者:苏鹏飞 徐松毅 于晓磊
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。
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
中图分类号:TN915.1 文献标识码:A DOI: 10.7535/hbgykj.2022yx01002
Abstract: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.
Keywords:computer neural network;whale optimization algorithm;LSTM neural network;narrowband communication network;network delay prediction
窄带通信网络是为某些特殊场景提供应急通信保障的低速通信系统的主要构成部分,其网络时延受到网络拓扑结构、气象变化因素、网络协议及路由算法等多方面因素影响,当网络拓扑结构、网络协议及路由算法固定下来之后,时间序列成为诱导其变化的主要影响因子。传统的窄带通信网网络协议单一,根据需求需要手动进行链路选择,随着窄带通信网的网络复杂度增加及多种网络协议的接入,迫切需要通过对窄带通信网网络时延预测,从而为窄带通信网的链路选择及网络协议的切换提供参考。目前,网络时延预测主要有基于数理统计的数学建模法,最小二乘支持向量机,神经网络算法。文献[1]通过ν臣剖据的回归分析和误差分析,提出了一种基于自回归求和滑动平均(ARIMA)模型,对网络化控制系统的随机时延进行预测,相较于ARMA模型精度有所提高;文献[2]提出了一种基于粒子群算法优化(PSO)的最小二乘法支持向量机(LS-SVM)算法,对列车通信网络的网络时延进行预测,但是PSO优化的参数维度较高,会影响预测时效性;文献[3]运用BP神经网络,同时运用PSO算法对神经网络权值和阈值进行优化,通过机器学习的方法对归一化的网络时延数据进行预测,但BP神经网络没有记忆性的特点,使得其只能通过前两个时序的时延数据预测下一时刻的网络时延,无法关联前面更长时间时序数据的特征。对此,本文选取单一对流层散射通信链路构成的窄带通信网络,提出了长短期记忆神经网络(LSTM)算法,关联长短期各个时序的网络时延的历史数据,通过鲸鱼优化算法(WOA)优化LSTM神经网络的学习率,隐含层神经元个数和最大训练次数,提高算法预测精度,对其网络时延进行预测。
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