中文摘要：

小时间尺度的网络流量的混沌性被噪声掩盖难以预测,本文通过局部投影降噪得到可预测的混沌性流量趋势.针对网络流量存在的时变性和长周期性,提出一种最优样本子集在线模糊最小二乘支持向量机（Least SquaresSupport VectorMachine,LSSVM）预测方法：以与预测样本时间上以及欧式距离最近的样本点构成最优样本子集,并对其模糊化处理,最后采用模糊LSSVM训练获得预测模型.通过分块矩阵降低预测模型在线更新的运算复杂度.对真实网络流量的降噪以及预测的结果表明本文方法能够快速准确的预测网络流量趋势.

英文摘要：

The chaotic performance of small-time scale network traffic was covered by noise, which made the traffic tmpre- dictable. This paper introduces the local projection to denosie network traffic; a chaotic and predictable traffic trend is obtained. As the network Iraffic series is long-period and time-varying, a new method named optimal training subset online fuzzy least squares support vector machines （OTSOF-LSSVM） is proposed. Samples temporal and distance nearest to prediction sample are chosen as optimal training subset, and the subset are fuzzified. On this basis, the prediction model is established by fuzzy LSSVM. The model update computational complexity is reduced by partitioned matrix calculation. The noise reduction and trend prediction on network traffic shows the proposed method can predict the trend quickly and exactly.