Network Traffic Prediction Using Wavelet Denoising and Optimized Support Vector Machine

被引：0

作者：

Tian Z. ^{[1
]}

Pan X. ^{[1
]}

机构：

[1] School of Artificial Intelligence, Shenyang University of Technology, Shenyang

来源：

Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2022年 / 45卷 / 05期

关键词：

Improved slime mould algorithm; Network traffic; Prediction; Support vector machine; Wavelet denoising;

D O I：

10.13190/j.jbupt.2021-146

中图分类号：

学科分类号：

摘要：

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. © 2022, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.

引用

页码：79 / 84

页数：5

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