基于小波支持向量机的VBR视频流量预测

：VBR视频流量具有时变性、突发性和非线性等变化特点,为了提高VBR视频流量的预测精度,提出一种小波支持向量机的VBR视频流量预测模型(WSVM)。首先对VBR视频流量时间序列进行相空间重构,然后将其输入到小波支持向量机进行学习,建立VBR视频流量预测模型,最后采用仿真实验对模型性能进行测试,并与支持向量机、小波神经网络进行对比。仿真结果表明,相对于其它预测模型,WSVM模型提高了VBR视频流量预测精度,能够更加准确反映VBR视频流量的复杂变化规律。     

MPEG2-TS VBR码流质量监测技术

MPEG2-TS VBR编码技术日益广泛使用,但传统的基于MDI-DF的算法不太适合检测VBR码流的传输质量问题.提出了MPEG2-TS VBR码流质量评估算法,通过监测MPEG2-TS码流的PCR字段,计算在单位时间内接收到的VBR视频流的可播放时长与单位时间的绝对偏差量,根据绝对偏差量评价VBR视频流传输质量.实验结果证明该算法具有可行性和时效性.     

用神经网络实现VBR视频通信量的在线预测

VBR(Varible Bit Rate)视频信号具有时变性、非线性和突发性等特点,实现该信号通信量的高精度预测难度较大.针对以上问题,本文提出了一种用于VBR视频通信量预测的自适应神经网络模型,网络训练采用离线与在线相结合的方式,同时通过删除不重要的权重,以优化网络的拓扑结构,提高网络的推广能力,降低网络在线学习的计算复杂度;对VBR视频通信量预测的模拟结果表明该模型具有高的预测精度,并能满足通信系统对预测实时性的要求.     

基于小波支持向量机的VBR视频流量预测模型

针对VBR视频流量的时变性、突发性和非线性等特点,提出一种基于小波支持向量机的VBR视频流量预测模型(WSVM)。首先对VBR视频流量时间序列进行相空间重构,然后将其输入到小波支持向量机进行学习,建立VBR视频流量预测模型,最后采用仿真实验对模型性能进行测试。结果表明,相对于对比模型,WSVM提高了VBR视频流量预测精度,更加准确地描述了VBR视频流量的复杂变化特点。     

基于L—MBP神经网络的VBR业务预测

ATM网络中,利用实时预测技术进行动态带宽分配,可以极大地提高ATM网络资源的利用率以及VBR业务的QOS保障。针对传统BP算法训练周期冗长的缺点,本文提出用L-MBP神经网络对VBR业务进行实时预测,文中给出了VBR业务的预测模型,并对该模型进行了仿真。结果表明,本文给出的模型显著地改变了神经网络的学习速度。     

自相似VBR视频源业务流的建模和仿真

该文在对实际VBR MPEG视频源统计特性分析的基础上,参照分形高斯噪声自相似(Fractional Gaussian Noise Self-Similar)模型产生方法,实现了对ATM网络中最主要业务流VBR视频源流的建模,提出了改进方法,使得对实际源的仿真不仅考虑到了长期相关性,同时也兼顾到了短期相关性。仿真结果表明,经改进的自相似VBR视频源模型是一种较理想的模型。     

用支持向量机网络实现VBR视频通信量的预测

VBR(Variable Bit Rate)视频信号具有时变性、非线性和突发性等特点,实现该信号通信量的高精度预测是提高信息传输速度和提高网络带宽资源利用效率的重要手段.针对以上问题,本文提出了一种用于VBR视频通信量预测的差分输入支持向量机(SVM:Support Vector Machine)网络模型.该网络模型采用结构风险最小化准则,在最小化经验风险的同时,尽量缩小模型预测误差的上界,从而使网络模型具有更好的推广能力.实验结果表明:支持向量机网络模型的预测误差为0.0018,而梯度径向基函数(Gradient Radial Basis Function:GRBF)神经网络模型的预测误差为0.0029.可以看出,支持向量机网络模型的预测精度要比GRBF网络模型的预测精度高出大约40%.     

基于VBR视频流量预测技术的DVB-H复用器调度算法

DVB-H采用的时间分片技术对现有的复用器算法提出了挑战.本文在VBR视频流量预测技术的基础上提出了基于VBR视频流量预测技术的DVB-H网络复用器调度算法.本算法根据VBR视频码率的预测值,预先分配下一调度周期的带宽,满足了时间分片技术对复用器调度算法的要求.与CBR调度算法相比,本算法具有较小的时延和较高的带宽利用率.     

用于VBR视频通信量预测的梯度径向基函数网络模型

提出采用梯度径向基函数(GRBF,gradientradialbasisfunction)神经网络实现VBR(variablebitrate)视频通信量的预测,由于GRBF神经网络采用差分输入,能够消除由于局部平均值随时间变化而造成的不稳定性,特别适合于非平稳时间序列预测。仿真结果显示,GRBF神经网络模型的预测误差(相对均方误差)为2.9×10-3,而其它几种常见预测模型的预测误差在(1.6～8.5)×10-2之间。     

VBR视频在IPTV点播业务中的应用

通过对VBR视频流量进行工程建模,结合IPTV用户点播时间间隔分析聚合VBR视频流量特性,研究表明在兼容现有终端和平台系统的情况下,即使不采用任何视频平滑处理,在IPTV点播业务中通过应用大规模VBR视频流的统计随机特性进行自我平均(self-average)也可获得30%~50%的统计增益,并通过大规模现场试验验证了VBR视频自平均特性在IPTV点播业务中的可行性和有效性。     

基于神经网络模型的网络流量预测综述

网络流量预测技术可以帮助运营商准确预估网络的使用情况,合理分配并高效利用网络资源,以满足日益增长且多样化的用户需求。大量研究表明,神经网络的预测性能高于其他经典预测方法,在网络流量预测中的应用潜力巨大。总结网络流量预测的一些关键因素和预测误差评判方法,重点介绍近年提出的可用于预测的神经网络模型原理、相关预测方法及其属性比较,旨在为包括流量在内的各种网络参数预测技术的学术研究与实际应用提供可借鉴的方法和手段。     

小时间尺度网络流量混沌性分析及趋势预测

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 Squares Support Vector Machine,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...     

Real-time VBR video traffic prediction for dynamic bandwidth allocation

In this paper, we systematically investigate the long-term, online, real-time variable-bit-rate (VBR) video traffic prediction, which is the key and complicated component for advanced predictive dynamic bandwidth control and allocation framework for the future networks and Internet multimedia services. We focus on neural network-based approach for traffic prediction and demonstrate that the prediction performance and robustness of neural network predictors can be significantly improved through multiresolution learning. We show that neural network traffic predictor trained through the multiresolution learning (called multiresolution learning NN traffic predictor) can successfully predict various real-world VBR video traffic up to hundreds of frames in advance, which then lays a solid foundation for predictive dynamic bandwidth control and allocation mechanism. Also, dynamic bandwidth control/allocation based on long-term traffic prediction is discussed in detail.     

多媒体通信中智能化媒体内同步机制

本文提出了一种智能化视频流量的预测和同步机制（ＩＦＳＭ）,它由ＢＰ神经网络流量预测器（ＢＰＮＮ）、输出缓冲区和基于模糊神经网络（ＦＮＮ）的输出速率决策器所组成。ＢＰＮＮ采用一种在线训练的ＢＰ神经网络预测在将来的一定时间间隔（ＦＩ）内的平均分组速率,ＦＮＮ决策器根据预测的流量特性和缓冲区中的分组数动态地调节下一个分组输出的时间。仿真结果表明：与窗口机制相比,ＩＦＳＭ能够使视频流量取得较高的连续性和较低的时延,并且由于ＦＮＮ的学习能力,ＩＦＳＭ可以自适应地调节相应参数以满足不同的服务质量的要求。     

基于神经网络的业务量预测研究

分别采用back—propagation（BP）算法和Favidon最小二乘学习算法训练神经网络（NN），并用于复杂业务流量预测。以自相似流量模型验证了2种NN学习算法的有效性，并分析比较了他们在流量预测中的可行性，得出Davidon最小二乘学习算法训练的NN比BP算法收敛速度快、收敛误差相差不多，验证了复杂自相似业务流的可预测性，为复杂自相似网络业务流预测的研究提供了一种有效途径。     

Optimal nonlinear adaptive prediction and modeling of MPEG video inATM networks using pipelined recurrent neural networks

This paper investigates the application of a pipelined recurrent neural network (PRNN) to the adaptive traffic prediction of MPEG video signal via dynamic ATM networks. The traffic signal of each picture type (I, P, and B) of MPEG video is characterized by a general nonlinear autoregressive moving average (NARMA) process. Moreover, a minimum mean-squared error predictor based on the NARMA model is developed to provide the best prediction for the video traffic signal. However, the explicit functional expression of the best mean-squared error predictor is actually unknown. To tackle this difficulty, a PRNN that consists of a number of simpler small-scale recurrent neural network (RNN) modules with less computational complexity is conducted to introduce the best nonlinear approximation capability into the minimum mean-squared error predictor model in order to accurately predict the future behavior of MPEG video traffic in a relatively short time period based on adaptive learning for each module from previous measurement data, in order to provide faster and more accurate control action to avoid the effects of excessive load situation. Since those modules of PRNN can be performed simultaneously in a pipelined parallelism fashion, this would lead to a significant improvement in the total computational efficiency of PRNN. In order to further improve the convergence performance of the adaptive algorithm for PRNN, a learning-rate annealing schedule is proposed to accelerate the adaptive learning process. Another advantage of the PRNN-based predictor is its generalization from learning that is useful for learning a dynamic environment for MPEG video traffic prediction in ATM networks where observations may be incomplete, delayed, or partially available. The PRNN-based predictor presented in this paper is shown to be promising and practically feasible in obtaining the best adaptive prediction of real-time MPEG video traffic     

Predictive dynamic bandwidth allocation for efficient transport ofreal-time VBR video over ATM

This paper presents a novel approach to dynamic transmission bandwidth allocation for transport of real-time variable-bit-rate video in ATM networks. Video traffic statistics are measured in the frequency domain. The low-frequency signal captures the slow time-variation of consecutive scene changes while the high-frequency signal exhibits the feature of strong frame autocorrelation. Our queueing study indicates that the video transmission bandwidth in a finite-buffer system is essentially characterized by the low-frequency signal. We further observe in typical JPEG/MPEG video sequences that the time scale of video scene changes is in the range of a second or longer, which localizes the low-frequency video signal in a well-defined low-frequency band. Hence, in a network design it is feasible to implement dynamic allocation of video transmission bandwidth using on-line observation and prediction of scene changes. Two prediction schemes are examined: recursive least square method and time delay neural network method. A time delay neural network with low-complexity high-order architecture, called “pi-sigma network,” is successfully used to predict scene changes. The overall dynamic bandwidth-allocation scheme presented is shown to be promising and practically feasible in obtaining efficient transmission of real-time video traffic     

RBF神经网络在视频业务建模及预测中的应用

ＡＴＭ技术在关键在于业务控制,能否有效地实施业务控制则取决于对业务特征的了解和预测能力。传统的解析方法对视频业务进行预测的局限性较为明显。本文采用径向基函数神经网络对视频业务进行建模和预测,并提出了分别采用ＬＢＧ算法和Ｈｅｓｔｅｎｅｓ奇异值解进行隐含层神经元中心选择和输出神经腱权值计算的改进方法。     

Evaluation of neural network architectures for MPEG-4 video traffic prediction

Multimedia applications and particularly MPEG-coded video streams are becoming major traffic components in high speed networks. Traffic prediction is important in enhancing the reliable operation over these networks. However, MPEG video traffic exhibits periodic correlation structure and a complex bit rate distribution, making prediction difficult. Neural networks can effectively be used to overcome such problem. In the literature, the problem has been mostly evaluated using standard feed-forward neural networks. However, a significant improvement can be expected using different types of neural networks. In this paper, six separate neural network predictors (including feed-forward) that can predict the basic frame types of MPEG-4: I, P, and B are developed and evaluated using long entertainment and broadcast video sequences. The performance is also compared to the widely used linear predictor. Comparison with results published in a recent work is also presented.