【作者】 陆国浩；

【导师】 朱艳琴；

【作者基本信息】 苏州大学 ， 计算机技术， 2007， 硕士

【摘要】 目前网络行为特征日趋复杂,随着Internet飞速发展,这给网络规划、网络管理带来了巨大的挑战,矛盾也日益突出,进行网络流量分析进而建立有效的网络流量模型是当前网络研究的热点问题之一。网络流量模型是对网络流量特性的刻画,一个好的模型不仅应能准确地反映出历史流量的特性,而且还能有效预测将来一段时间内网络流量的大小。一些传统模型通常假定网络流量满足线性关系,然后用线性递推和组合的方法来描述系统。实际上网络流量数据随时间变化并未表现出显著规律,因为它包含了很多非线性因素。本文围绕提高预测精度和自适应能力的主题,在网络流量预测中采用非线性模型,作了深入的分析,并在此基础上设计并实现了网络流量预测分析系统。本文在引入时间序列预测理论和人工神经网络理论的基础上,设计出基于BP神经网络的流量预测模型,给出了有效流量预测方案。本文在预测模型中加入了动态调用预测功能,使得所设计网络流量预测系统在灵活性和高效性方面得到了提升,并且具备较强的自适应能力。本文所设计的预测系统通过大量实验获得最佳预测模型,应用该系统能有效预测短期内网络流量的变化,进行信息预报,为网络规划、网络管理做出了积极有益探索。更多还原

【Abstract】 At present, the features of network behavior are becoming increasing complicated, which, with the rapid development of Internet, brings a great challenge to the network planning and network management. One of the present hot points is to create an effective network flow model for the network flow analysis. The network flow model is a depiction of the features of the network flow. A good model not only should accurately reflect the features of historic flow but also can predict the size of the network flow in a time to come. Some traditional models usually assume that the network flow meets with the linear relationship and then describe the system with the linear recursion and combination. In fact, the network flow data don’t show the remarkable law with the change of the time, because they contain many non-linear factors. This paper, around the self-adapting ability and increasing the accuracy of prediction, makes a detailed analysis with the non-linear model in the network flow prediction, and designs and realizes an analyzing system of network flow prediction on that basis.The network flow prediction system introduced in this paper, on the basis of introducing the theory of time series prediction and the theory of artificial neural network, designed a flow predicting model based on the BP-neural network, offering an effective predicting solution. The dynamic call predicting function in the predicting model improves the flexibility and efficiency of the system introduced in this paper, which is also of a stronger self-adapting ability.This system acquires a best predicting model through much experiment; by means of this system, an effective prediction can be made on the network flow in the short-term period and on the information, which offers an active and beneficial exploration in the network planning and network management.更多还原