In this paper, a method of network traffic prediction based on wavelet transform and autoregressive model is proposed.
本文前言部分，主要介绍了网络流量预测的研究背景及本文的工作。
The experimental results prove that the model is efficient in network traffic prediction with good astringency and stability.
实验结果表明，该模型对网络流量的短期预测是有效可行的，并具有良好的收敛性和稳定性。
The traditional methods of network traffic prediction are to build mathematical models using its statistical characteristics.
网络流量预测传统方法是利用流量的统计特性建立数学模型。
Based on the introduction to neural network principle, two methods of solving free train schedule and traffic flow prediction in transportation system with neural network are analyzed.
在简要介绍神经网络原理的基础上，分析了采用神经网络解决交通系统中空车调度及交通流预测的原理及方法。
Traffic prediction has significant meanings for management, layout and design of largescale network.
网络流量预测对大规模网络管理、规划、设计具有重要意义。
Method named BAYESIAN combined neural network model is proposed for short term traffic flow prediction in this paper.
提出一种新的贝叶斯组合神经网络模型并将其应用于短期交通流量的预测。
This paper focuses on the ABR(Available bit rate) congestion control algorithm in the satellite ATM network based on onboard processing, which is implemented by using the traffic prediction.
本文主要研究基于星上处理的卫星ATM网的ABR流量控制，它通过对流量的长时预测来达到控制目的。
As one of the main contents of service management, traffic prediction plays an important role in network layout, traffic management etc.
网络流量预测在网络规划、流量管理等方面起着重要的作用，是业务管理的主要研究内容之一。
In this paper, the time - sequence model of traffic flow is based on the improved BP neural network, and this model can be used for short time prediction of traffic flow.
本文采用改进型BP神经网络建立起交通流的时间序列模型，该模型可用于短期内道路交通流量的预测。
A large number of techniques have been applied into short-term traffic flow prediction, which can be classified into two groups: statistical models and artificial neural network model.
介绍了用于短期交通流预测的两大类模型：统计预测算法和人工神经网络模型。
By using some road traffic noise measured data, a model of neural network for road traffic noise prediction was established.
本文通过分析影响公路交通噪声的各种因素，并利用实测样本数据，建立了一个公路交通噪声预测的神经网络模型。
Aiming at the issue about multi-step prediction of the traffic flow chaotic time series, a fast learning algorithm of wavelet neural network (WNN) based on chaotic mechanism is proposed.
针对交通流量混沌时间序列多步预测的问题，提出了一种基于混沌机理的小波神经网络(WNN)快速学习算法。
Accordingly, this paper proposes a fusion-prediction model of traffic-flow in urban road-intersection based on integrated ANN (Artificial Neural Network).
基于此，本文提出了基于集成神经网络的城市道路交通流量的融合预测模型。
The prediction of network traffic flow is a problem of great significance in the research work of resource allocation and congestion control.
在高速网络资源分配与拥塞控制研究中，网络业务流量的预报是一个具有重要意义的课题。
The key problems of vehicle navigation dynamic path planning are traffic network model, path planning algorithm and traffic flow prediction.
交通网络模型、路径规划算法以及交通流预测是车辆导航动态路径规划需要解决的重点问题。
Purpose of the paper: The paper presents a real-time online prediction model for network traffic based on ARMA, deriving analysis of the prediction model is carried out;
提出基于ARMA的网络流量实时在线预测模型，并对该预测模型进行了推导分析；
The traffic prediction in Wireless Sensor Network(WSN) is important to WSN management.
无线传感器网络（WSN）的流量预测研究对WSN管理有重要的意义。
For instance, it is very well suited for the prediction of network-traffic, economic information and other datum in non-linear system.
这种方法可用于动态数据预测的各个领域，如网络流量的预测、经济信息的预测以及其它非线性系统的预测。
For instance, it is very well suited for the prediction of network-traffic, economic information and other datum in non-linear system.
这种方法可用于动态数据预测的各个领域，如网络流量的预测、经济信息的预测以及其它非线性系统的预测。
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