北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2022, Vol. 45 ›› Issue (5): 79-84.

• 论文 • 上一篇    下一篇

小波消噪和优化支持向量机的网络流量预测

田中大,潘信澎   

  1. 沈阳工业大学 人工智能学院
  • 收稿日期:2021-07-19 修回日期:2022-01-27 出版日期:2022-10-28 发布日期:2022-11-01
  • 通讯作者: 田中大 E-mail:tianzhongda@126.com
  • 基金资助:
    辽宁省教育厅高等学校基本科研项目重点项目;辽宁省自然科学基金项目

Network Traffic Prediction Using Wavelet Denoising and Optimized Support Vector Machine

TIAN Zhongda, PAN Xinpeng   

  • Received:2021-07-19 Revised:2022-01-27 Online:2022-10-28 Published:2022-11-01
  • Contact: Zhong-Da Tian E-mail:tianzhongda@126.com

摘要: 为了提高对网络流量的预测精度,提出了一种小波消噪和改进黏菌算法优化支持向量机的网络流量预测模型首先应用小波消噪对网络流量进行消噪处理,采用支持向量机作为预测模型由于支持向量机预测结果受模型参数影响较大,采用带有随机惯性权重机制的改进黏菌算法来优化支持向量机模型中惩罚因子以及核函数参数对所提模型使用最佳参数进行仿真实验,并利用实际采集的网络流量数据进行验证实验结果表明,所提模型在评估指标上均优于对比模型

关键词: 网络流量, 预测, 小波降噪, 支持向量机, 改进黏菌算法

Abstract: In order to improve the accuracy of network traffic prediction, a network traffic prediction model is proposed based on wavelet denoising and improved slime mold algorithm optimized support vector machine. First, wavelet denoising is used to denoise network traffic, and support vector machine is used as the prediction model. Since the prediction results of support vector machine are greatly affected by the model parameters, an improved slime mold algorithm with random inertia weight is used to optimize the penalty factor and kernel function parameters that used in the support vector machine model. The validity of the proposed model is verified by the collected network traffic. The simulation results show that the proposed model is superior to the comparison model in terms of the evaluation index.

Key words: network traffic, prediction, wavelet denoising, support vector machine, improved slime mould algorithm

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