摘要
高精度网络流量预测是现代网络智能管理的基础,针对支持向量机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,提出了改进灰狼算法优化支持向量机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,然后引入改进灰狼算法快速搜索到全局最优支持向量机的相关参数,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,建立能够挖掘网络流量历史数据包含变化规律的预测模型,最后与其他算法优化支持向量机的网络流量预测模型进行了对比分析。结果显示,改进灰狼算法优化支持向量机的网络流量预测精度超过90%,远高于对比模型,且预测建模过程的建模时间少于对比模型,可以满足网络流量管理的高精度和实时性的要求。
High precision network traffic prediction is the basis of modern network intelligent management. Targeting at the problem of parameter optimization of SVM in the process of network traffic prediction modeling to improve the network traffic prediction results, this paper proposes the network traffic prediction model of SVM optimized by Improved Gray Wolf algorithm. Firstly, collect the historical data of network traffic, and preprocess the data with phase space reconstruction and normalization, then introduce the improved gray wolf algorithm to quickly search the relevant parameters of the global optimal support vector machine, and learn the historical data of network traffic after preprocessing according to the optimal parameters, and establish a prediction model that can mine the history data of network traffic including the law of change after that, the network traffic prediction model of SVM optimized by other algorithms is compared and analyzed. The results show that the prediction accuracy of the improved gray wolf algorithm optimized support vector machine is more than 90%, much higher than the compared model, and the training time of the prediction modeling process is less than the compared model, which can meet the requirements of high accuracy and real-time network traffic management.
作者
杨晓敏
Yang Xiaomin(Maths&Information Technology School,Yuncheng University,Yuncheng 044000,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2021年第3期211-217,共7页
Journal of Electronic Measurement and Instrumentation
基金
运城学院协同创新项目(2015016)
运城学院应用研究项目(CY-2020028)
山西省教育科学“十三五”规划项目(HLW-20101)资助。
关键词
现代网络
改进灰狼算法
相空间重构
历史样本数据
支持向量机
全局最优参数
modern network
improved gray wolf algorithm
phase space reconstruction
historical sample data
support vector machine
global optimal parameters
