基于特征提取相似日的ELM短期负荷预测研究

为解决短期电力负荷预测中预测精度差、计算时间长等问题,提出一种基于自组织特征映射网络进行特征提取相似日的极限学习机短期电力负荷预测方法。通过自组织特征映射网络找出与预测日同类型的历史数据作为训练样本;并采用预测能力强、计算时间短的ELM网络进行预测。以某市电力负荷数据进行仿真,并将上述方法与传统神经网络进行对比。仿真算例表明,基于特征提取相似日的ELM方法具有较高的预测精度,泛化性能好,且运算时间短。     

Dynamical protection design with P-cycle parallel heuristic algorithm for optical networks

研究并总结了现有的各种自相似流量分析和预测的方法,同时采集局域网的动态流量数据,在此基础上生成自相似业务流.结合当今计算机界正在蓬勃发展中的并行计算,设计相关P圈配置并行算法,并将其应用到光网络的动态仿真中.     

一种基于预测PI控制器的自相似网络主动队列管理算法

近年研究发现网络中的业务量呈自相似特征,这种自相似特征显著影响网络的流量控制与排队性能,本文在自相似网络流量可预测的基础上,利用线性回归分析理论进行流量预测,并应用控制理论中的预测PI控制器原理设计了动态矩阵PI控制主动队列管理(Dynamic Matrix PI Control-Active Queue Management,简称DMPIC-AQM)算法,以克服队列的剧烈振荡,保持队列稳定在期望的长度.仿真实验结果表明,DMPIC-AQM算法在网络流量剧烈变化和小期望队列长度的情形下,DMPIC-AQM算法明显优于RED与PI算法.     

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。     

基于自相似业务的网络TCP拥塞控制算法

本文通过对自相似网络模型的分析,采用TCP协议可以直接观测的回环时间（Bound Trip Time)来进行拥塞程度预测,提出一种改进的TCP拥塞控制算法。仿真证明,改进算法在一定程度上提高了网络的整体性能,在自相似程度较高时尤为明显。     

蓝牙无线个域网及其流量特性分析

对蓝牙无线个域网的各项流量特性进行了分析,探讨了蓝牙设备在4种工作模式间的切换对蓝牙网络性能的影响,分析了影响蓝牙网络有效带宽的各个因素。另外,通过仿真,发现蓝牙无线链路流量特性具有自相似的特性,即适合采用自相似类型的流量模型来进行分析、描述及预测。     

基于模糊神经网络的网络业务量预测研究

利用神经网络(NN)的自学习能力以及模糊逻辑的动态性和及时性等特点,将模糊逻辑和 NN 有机地结合起来,构造出了五层模糊神经网络(FNN),并用训练 NN 的相应学习算法-BP 算法来训练网络。本文将 FNN 用于网络自相似业务预测研究中,并与单纯的 NN 算法相比较。仿真结果表明,FNN 能很好地预测复杂网络业务,与传统的 NN 算法相比,不仅收敛速度快,且得到更好的预测效果。本文为复杂网络业务流量预测研究提供了一种有效途径。     

基于EMD及ARMA的自相似网络流量预测

提出了一种基于ARMA(自回归滑动平均)模型的经验模式分解预测自相似网络流量的方法,进行了理论证明和仿真验证.结果表明,经验模式分解对长相关流量有去相关的作用,采用ARMA模型即可对自相似网络流量准确刻画,不但降低了算法的复杂度,而且预测精度高于径向基函数神经网络的预测精度.     

自相似业务下共享通道保护WDM网络性能分析

该文利用随机中点置换-分形高斯噪声(RMD-FGN)方法生成自相似业务,并且利用分层图方法来记录WDM网络状态,提出了一种链路状态描述模型。对自相似业务下55 Mesh_Torus共享通道保护WDM网络性能进行了仿真。结果表明:当业务的自相似系数或方差变大时,也就是说业务的突发程度变大时,阻塞率变大,网络的性能下降。增加单纤波长数,可以降低阻塞率,提高网络性能。     

基于混合机器学习的电磁功率谱密度预测模型

功率谱密度(PSD)预测是频谱管理中的重要环节。由于功率谱密度具有高度的复杂性、非线性和不确定性,单一的预测模型很难确保预测的准确性和效率。为克服单一预测方法的不足,提出一种混合的机器学习模型,将自组织映射(SOM)网络与回归树(RT)相结合,以预测信号的功率谱密度。使用自组织映射网络将具有相似手工特征的原始样本集聚类成簇;将每一个簇分别构建回归树来预测功率谱密度;最后,使用亚琛工业大学的数据进行实验。结果表明,预测结果的均方根误差比现有方法提高0.824,证明混合模型具有较高的预测精确度和较好的泛化能力。     

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.     

Intelligent Video Traffic Smooth Mechanism for Multimedia Communication

1 Introduction Broad-band integrated service digital networks (B-ISDN) based on the asynchronous transfer mode (ATM) are designed to support a wide variety of multimedia with diverse statistical characteristics and quality of services (QoS). Among the var…     

Intelligent Video Traffic Smooth Mechanism for Multimedia Communication

A principal challenge in supporting real-time video services over ATM is the need to provide synchronous play-out in the face of stochastic end-to-end network delays. In this paper, an intelligent traffic smooth mechanism ( ITSM ) is proposed to meet the continuity requirement which is composed of a back-propagation neural network ( BPNN ) traffic predictor, a play-out buffer, and a fuzzy neural network ( FNN ) based play-out rate determinator. The BPNN traffic predictor online predicts the mean packet rate of the traffic in the future interval ( FI ) and the FNN is designed to adaptively determinate the play-out time according to the number of packets in the buffer and the traffic character predicted. Simulation results show that compared to the window mechanism, ITSM achieves high continuity with accepted delay. Furthermore, ITSM can be adaptively modified to meet the QoS of different kinds of services by FNN parameter training.     

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。     

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     

智能集成VBR MPEG视频流量预测模型

作为数字媒体网络视频通信的主要方式,VBR MPEG视频流量的预测能力是直接关系缓冲区设计、动态带宽分配及拥塞控制等提高网络服务质量的关键因素.因此针对MPEG视频流的复杂特性,充分利用人工智能方法的优势,提出并建立了基于模糊神经网络的智能集成VBR MPEG 视频流量预测模型.采用模糊预测模型提高预测精度,利用神经网络解决预测的实时性问题.实验结果表明,与标准AR预测模型相比,该模型预测的准确度和可靠性显著提高,且算法简单易于推广到其他方法中使用.     

A predictive flow control scheme for efficient network utilization and QoS

In this paper, we develop a new predictive flow control scheme and analyze its performance. This scheme controls the nonreal-time (controllable) traffic based on predicting the real-time (uncontrollable) traffic. The goal of the work is to operate the network in a low congestion, high throughput regime. We provide a rigorous analysis of the performance of our flow control method and show that the algorithm has attractive and useful properties. From our analysis we obtain an explicit condition that gives us design guidelines on how to choose a predictor. We learn that it is especially important to take the queueing effect into account in developing the predictor. We also provide numerical results comparing different predictors that use varying degrees of information from the network.     

Contradictory relationship between Hurst parameter and queueing performance (extended version)

Long Range Dependent (LRD) network traffic does not behave like the traffic generated by the Poisson model or other Markovian models. From the network performance point of view, the main difference is that LRD traffic increases queueing delays due to its burstiness over many time scales. LRD behavior has been observed in different types and sizes of networks, for different applications (e.g., WWW) and different traffic aggregations. Since LRD behaviour is not rare nor isolated, accurate characterization of LRD traffic is very important in order to predict performance and to allocate network resources. The Hurst parameter is commonly used to quantify the degree of LRD and the burstiness of the traffic. In this paper we investigate the validity and effectiveness of the Hurst parameter. To this end, we analyze the UCLA Computer Science Department network traffic traces and compute their Hurst parameters. Queueing simulation is used to study the impact of LRD and to determine if the Hurst parameter accurately describes such LRD. Our results show that the Hurst parameter is not by itself an accurate predictor of the queueing performance for a given LRD traffic trace.     

Intelligent video smoother for multimedia communications

Multimedia communications often require intramedia synchronization for video data to prevent potential playout discontinuity resulting from network delay variation (jitter) while still achieving satisfactory playout throughput. In this paper, we propose a neural network (NN) based intravideo synchronization mechanism, called the intelligent video smoother (IVS), operating at the application layer of the receiving end system. The IVS is composed of an NN traffic predictor, an NN window determinator, and a window-based playout smoothing algorithm. The NN traffic predictor employs an on-line-trained back-propagation neural network (BPNN) to periodically predict the characteristics of traffic modeled by a generic interrupted Bernoulli process (IBP) over a future fixed time period. With the predicted traffic characteristics, the NN window determinator determines the corresponding optimal window by means of an off-line-trained BPNN in an effort to achieve a maximum of the playout quality (Q) value. The window-based playout smoothing algorithm then dynamically adopts various playout rates according to the window and the number of packets in the buffer. Finally, we show that via simulation results and live video scenes, compared to two other playout approaches, IVS achieves high-throughput and low-discontinuity playout under a mixture of IBP arrivals     

Intelligent voice smoother for silence-suppressed voice overInternet

When transporting voice data with silence suppression over the Internet, the problem of jitter introduced from the network often renders the speech unintelligible. It is thus indispensable to offer intramedia synchronization to remove jitter while retaining minimal playout delay (PD). We propose a neural network (NN)-based intravoice synchronization mechanism, called the intelligent voice smoother (IVoS). The IVoS is composed of three components: (1) the smoother buffer; (2) the NN traffic predictor; and (3) the constant bit rate (CBR) enforcer. Newly arriving frames, assumed to follow a generic Markov modulated Bernoulli process (MMBP), are queued in the smoother buffer. The NN traffic predictor employs an online-trained back propagation NN (BPNN) to predict three traffic characteristics of every newly encountered talkspurt period. Based on the predicted characteristics, the CBR enforcer derives an adaptive buffering delay (ABD) by means of a near-optimal simple closed-form formula. It then imposes the delay on the playout of the first frame in the talkspurt period. The CBR enforcer in turn regulates CBR-based departures for the remaining frames of the talkspurt, aiming at assuring minimal mean and variance of distortion of talkspurts (DOT) and mean PD. Simulation results reveal that, compared to three other playout approaches, the IVoS achieves superior playout, yielding negligible DOT and PD, irrespective of traffic variation