本文選題：網(wǎng)絡流量　切入點：極限學習機　出處：《微電子學與計算機》2017年06期 　論文類型：期刊論文

【摘要】：針對當前網(wǎng)絡流量預測模型存在的缺陷,結(jié)合網(wǎng)絡流量的混沌特性,提出了小波變換和極限學習機的網(wǎng)絡流量預測模型(WA-ELM).首先采用小波變換對網(wǎng)絡流量時間序列進行處理,得到不同頻率特征的分量,并對各特征分量進行相空間重構(gòu),然后采用極限學習機對網(wǎng)絡流量各分量進行建模與預測,并對網(wǎng)絡流量分量的預測值進行小波重構(gòu),得到原始網(wǎng)絡流量的預測值,最后采用具體網(wǎng)絡流量預測結(jié)果進行了驗證,并與其他模型進行了對照測試.結(jié)果表明,WA-ELM獲得了比其他模型更高的網(wǎng)絡流量預測精度,而且網(wǎng)絡流量的預測結(jié)果更加穩(wěn)定,為網(wǎng)絡流量提供了一種新的建模工具.
[Abstract]:In view of the shortcomings of the current network traffic prediction model and the chaotic characteristics of the network traffic, a network traffic prediction model of wavelet transform and extreme learning machine is proposed. Firstly, wavelet transform is used to deal with the network traffic time series. The components of different frequency features are obtained, and each characteristic component is reconstructed in phase space. Then, the network traffic components are modeled and predicted by extreme learning machine, and the predicted value of network traffic component is reconstructed by wavelet transform. The prediction value of the original network traffic is obtained, and the results of network traffic prediction are verified by the concrete network traffic prediction results, and compared with other models, the results show that WA-ELM has higher network traffic prediction accuracy than other models. Moreover, the prediction results of network traffic are more stable, which provides a new modeling tool for network traffic.
【作者單位】： 河套學院理學系;
【分類號】：TP393.06

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