本文選題：網(wǎng)絡(luò)流量 + 預(yù)測(cè)　； 參考：《信息與控制》2016年01期

【摘要】：針對(duì)網(wǎng)絡(luò)流量預(yù)測(cè),提出一類基于自組織映射(self-organizing map,SOM)神經(jīng)網(wǎng)絡(luò)的局部自回歸(auto-regressive,AR)方法.根據(jù)SOM的聯(lián)想記憶在時(shí)域的推廣,在矢量量化臨時(shí)聯(lián)想記憶(vector-quantized temporal association memory,VQTAM)建模技術(shù)的基礎(chǔ)上,給出具有多個(gè)局部線性AR模型的AR-SOM方法,基于前K個(gè)獲勝神經(jīng)元用權(quán)值代替輸入向量建立單一時(shí)變局部AR模型的K-SOM方法,以及在完成數(shù)據(jù)向量聚類的同時(shí),更新多個(gè)局部AR模型系數(shù)的LLM(local linear map)-SOM方法.相對(duì)于全局模型,基于SOM神經(jīng)網(wǎng)絡(luò)的局部AR方法能夠靈活給出有效的監(jiān)督神經(jīng)結(jié)構(gòu),降低了計(jì)算復(fù)雜度.將本文方法應(yīng)用于不同的網(wǎng)絡(luò)流量預(yù)測(cè)實(shí)例中,并與現(xiàn)有方法相比,實(shí)驗(yàn)結(jié)果表明所提出的方法能有效地改善預(yù)測(cè)精度,且性能更好.
[Abstract]:For network traffic prediction, a class of auto-autoregressive (AR) method based on self-organizing mapSOM neural network is proposed. According to the extension of SOM's associative memory in time domain, based on the modeling technique of vector-quantized temporal association memory (VQTAM), an AR-SOM method with multiple local linear AR models is presented. The K-SOM method based on the weights of the first K winning neurons instead of the input vectors to establish a single time-varying local AR model, and the LLM (local linear map) -SOM method to update the coefficients of multiple local AR models at the same time as the data vector clustering is completed. Compared with the global model, the local AR method based on SOM neural network can provide the effective supervised neural structure flexibly and reduce the computational complexity. The proposed method is applied to different network traffic prediction examples. Compared with the existing methods, the experimental results show that the proposed method can effectively improve the prediction accuracy and the performance is better.
【作者單位】： 蘭州交通大學(xué)自動(dòng)化與電氣工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(51467008)
【分類號(hào)】：TP393.06;TP183

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