本文選題：網(wǎng)絡(luò)流量　切入點：極限學(xué)習(xí)機　出處：《微電子學(xué)與計算機》2017年06期 　論文類型：期刊論文

【摘要】：針對當(dāng)前網(wǎng)絡(luò)流量預(yù)測模型存在的缺陷,結(jié)合網(wǎng)絡(luò)流量的混沌特性,提出了小波變換和極限學(xué)習(xí)機的網(wǎng)絡(luò)流量預(yù)測模型(WA-ELM).首先采用小波變換對網(wǎng)絡(luò)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(xué)習(xí)機對網(wǎng)絡(luò)流量各分量進行建模與預(yù)測,并對網(wǎng)絡(luò)流量分量的預(yù)測值進行小波重構(gòu),得到原始網(wǎng)絡(luò)流量的預(yù)測值,最后采用具體網(wǎng)絡(luò)流量預(yù)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(luò)流量預(yù)測精度,而且網(wǎng)絡(luò)流量的預(yù)測結(jié)果更加穩(wěn)定,為網(wǎng)絡(luò)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(xué)院理學(xué)系;
【分類號】：TP393.06

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