期刊文献+

基于灰狼算法优化深度学习网络的网络流量预测 被引量:10

Grey Wolf Algorithm to Optimize Network Traffic Prediction of Deep Learning Network
下载PDF
导出
摘要 针对深度学习网络在网络流量预测建模过程中的参数优化难题,以改善网络流量预测结果为目标,提出一种基于改进灰狼算法优化深度学习网络的网络流量预测模型.首先,收集网络流量历史数据,并对数据进行相空间重构、归一化等预处理;其次,引入灰狼算法快速搜索到全局最优深度学习网络的相关参数,并根据最优参数对预处理后的网络流量历史数据进行学习,建立能挖掘网络流量历史数据变化规律的预测模型;最后,与其他算法优化深度学习网络的网络流量预测模型进行对比分析.实验结果表明,基于改进灰狼算法优化深度学习网络的网络流量预测精度超过90%,远高于其他对比模型,且预测建模过程的建模时间少于对比模型,可满足网络流量管理的高精度和实时性要求. Aiming at the parameter optimization problem of deep learning network in the process of network traffic prediction modeling,in order to improve the network traffic prediction results,we proposed a network traffic prediction model based on improved gray wolf algorithm to optimize the deep learning networ k.Firstly,the historical data of network traffic was collected and preprocessed by phase space reconstruction and normalization.Secondly,gray wolf algor ithm was introduced to quickly search the relevant parameters of the global optimal deep learning network,the preprocessed historical data of network traffi c was learned according to the optimal parameters,and a prediction model that could mine the change law of historical data of network traffic was established.Finally,the network traffic prediction model of deep learning network optimized by other algorithms was compared and analyzed.The experimental results show that the network traffi c prediction accuracy based on improved gray wolf algorithm to optimize deep learning network is more than 90%,which is much higher than other comparison models,and the modeling time of prediction modeling process is less than that of comparison model,which can meet the requirements of high accuracy and real-time of network traffic management.
作者 张志宏 刘传领 ZHANG Zhihong;LIU Chuanling(School of Information Technology,Shangqiu Normal University,Shangqiu 476000,Henan Province,China)
出处 《吉林大学学报(理学版)》 CAS 北大核心 2021年第3期619-626,共8页 Journal of Jilin University:Science Edition
基金 河南省科技厅基础前沿项目(批准号:122300410373)。
关键词 现代网络 改进灰狼算法 相空间重构 历史样本数据 深度学习网络 全局最优参数 modern network improved gray wolf algorithm phase space reconstruction historical sample data deep learning network global optimal parameter
  • 相关文献

参考文献15

二级参考文献109

共引文献137

同被引文献81

引证文献10

二级引证文献24

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部