小波消噪和优化支持向量机的网络流量预测

Network Traffic Prediction Using Wavelet Denoising and Optimized Support Vector Machine

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。

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.