本文選題：網(wǎng)絡(luò)流量　切入點(diǎn)：混沌　出處：《科技通報(bào)》2014年02期 　論文類型：期刊論文

【摘要】：在網(wǎng)絡(luò)預(yù)測算法中傳統(tǒng)的預(yù)測幾乎都沒有考慮流量的自相似性和高斯性,僅僅利用最大Lyapunov指數(shù)進(jìn)行計(jì)算機(jī)網(wǎng)絡(luò)流量的混沌性檢驗(yàn),對網(wǎng)絡(luò)流量的預(yù)測也僅僅是以計(jì)算得到的最大Lyapunov指數(shù)為前提,算法精度受限。提出一種改進(jìn)的Wolf一步預(yù)測算法,對網(wǎng)絡(luò)流量通過自相似的FGN(FGN,fractional gaussian noise)過程處理,得到替代原網(wǎng)絡(luò)流量的新的序列,新的替代流量序列具有自相似性,從而進(jìn)行預(yù)測。仿真結(jié)果準(zhǔn)確檢驗(yàn)了網(wǎng)絡(luò)流量的混沌性,預(yù)測結(jié)果表明,改進(jìn)的預(yù)測算法在略有縮短最大預(yù)報(bào)時(shí)間下,精度卻高很多,預(yù)測的誤差小于3%的點(diǎn)比例比原傳統(tǒng)算法提高了20%以上。
[Abstract]:In the traditional network prediction algorithms, the self-similarity of traffic and Gao Si are hardly taken into account, and the chaos of computer network traffic is only tested by using the largest Lyapunov exponent. The prediction of network traffic is only based on the calculation of the largest Lyapunov exponent, and the accuracy of the algorithm is limited. An improved Wolf one-step prediction algorithm is proposed to process network traffic through the self-similar FGNN FGNN gaussian noiseal process. A new sequence is obtained to replace the original network traffic, and the new alternative traffic sequence has self-similarity, so it can be predicted. The simulation results verify the chaos of the network traffic accurately, and the prediction results show that, The precision of the improved prediction algorithm is much higher than that of the traditional algorithm, and the prediction error is less than 3%. The accuracy of the improved prediction algorithm is 20% higher than that of the traditional algorithm.
【作者單位】： 首都經(jīng)濟(jì)貿(mào)易大學(xué)信息工程系;
【分類號(hào)】：TP393.06

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本文編號(hào)：1561103