A NEW ADMISSION CONTROL APPROACH BASED ON PREDICTION

Admission control plays an important role in providing QoS to network users.Mo-tivated by the measurement-based admission control algorithm,this letter proposed a new ad-mission control approach for integrated service packet network based on traffic prediction .In the letter ,FARIMA(p,d,q,)models in the admission control algorithm is deployed.A method to simplify the FARIMA model fitting procedure and hence to reduce the time of traffic modeling and prediction is suggested.The feasibility-study experiments show that FARIMA models which have less number of parameters can be used to model and predict actual traffic on quite a large time scale.Simulation results validate the promising approach.     

改进的基于小波变换和FARIMA模型的网络流量预测算法

提出了一种改进的基于小波变换和FARIMA模型的网络流量预测算法,先对经过预处理的流量进行小波分解,再进行Mallat算法单支重构,接着用FARIMA模型分别对重构后的单支进行预测,最后合成流量。该算法较之传统的首先用FARIMA模型对小波系数进行预测再进行小波重构的算法减小了预测误差。仿真实验也验证了改进算法的预测准确性。     

基于FARIMA-GARCH模型的网络业务预测算法

网络流量的波动性与自相似特性为其精确预测提出了挑战。为此,提出了一种基于FARIMA-GARCH模型的预测算法。该算法首先利用分段双向CUSUM检测算法对流量序列的均值进行有效检测,并在此基础上将序列零均值化;然后采用限定搜索法对分数差分阶数进行精确估计;在获得模型参数后,使用FARIMA-GARCH模型对网络流量进行预测。仿真实验表明,限定搜索法能够获得比传统算法更高的估计精度。随后采用真实网络流量验证了预测算法的性能,在保持与FARIMA预测算法等价的时间复杂度下,其均方根和相对均方根误差与RBF神经网络预测算法相当,而高于FARIMA预测算法。同时,预测算法对突发流量的跟踪和预测性能明显优于对比算法,且有更好的区间估计性能。     

基于时间序列分析的弹道目标进动周期提取

利用微波暗室静态测量数据,反演了某型弹道目标在典型战情下的动态雷达散射截面积(RCS)时间序列.分析表明:该序列具有非平稳性、相关性和拟周期性的特点,直接利用谱分析方法提取进动周期的效果较差.采用B样条函数、广义自回归条件异方差(GARCH)模型和自回归-滑动平均模型(ARMA模型)构造了RCS序列的迭合滤波分解模型....     

互联网流量与两个相关模型

由于互联网具有多构性、突发连续性和自相似性等特征,使得用传统模型进行的排队分析、性能估计与实际网络有较大差距。本文介绍了两种互联网流量模型:AR IMA模型,FAR IMA模型,讨论了它们的适用范围、模型数学定义,描述了参数定阶推导等相关问题。     

改进的基于时变FARIMA模型和小波变换的网络流量预测算法

网络的流量特性是反映网络实时状态的一个重要特征,对于网络流量的分析、预测一直是该领域的研究热点。传统的基于时间序列模型的方法在计算效率和多尺度分析能力方面存在一定的局限性。本文提出了一种改进的基于小波变换和时变FARIMA模型的流量预测方法,利用小波变换的多尺度分析特性将原有的流量数据进行分解,在使用时变FARIMA模型进行预测,可大大提高算法的执行效率和预测的准确性。最后,本文选取了Bellcore提供的真实的网络流量进行了仿真实验,验证了提出的预测方法的准确性和有效性。     

一种基于ARMA 和NGARCH 过程的海杂波建模方法

使用自回归滑动平均(ARMA)和广义自回归条件异方差(GARCH)过程对金融数据建模是经济学常用手段。文中结合ARMA 过程和GARCH 过程的非线性化扩展模型,将其扩展到复数域,适合于海杂波建模应用。相比传统的海杂波模型及原始的GARCH 模型,文中提出的模型在概率密度函数拟合上具有明显的优势。此外,新模型还可准确地捕获相邻海杂波中存在的强相关性。实际雷达海杂波数据验证了该模型的准确性和有效性。     

Wavelet-based estimators of scaling behavior

Various wavelet-based estimators of self-similarity or long-range dependence scaling exponent are studied extensively. These estimators mainly include the (bi)orthogonal wavelet estimators and the wavelet transform modulus maxima (WTMM) estimator. This study focuses both on short and long time-series. In the framework of fractional autoregressive integrated moving average (FARIMA) processes, we advocate the use of approximately adapted wavelet estimators. For these "ideal" processes, the scaling behavior actually extends down to the smallest scale, i.e., the sampling period of the time series, if an adapted decomposition is used. But in practical situations, there generally exists a cutoff scale below which the scaling behavior no longer holds. We test the robustness of the set of wavelet-based estimators with respect to that cutoff scale as well as to the specific density of the underlying law of the process. In all situations, the WTMM estimator is shown to be the best or among the best estimators in terms of the mean-squared error (MSE). We also compare the wavelet estimators with the detrended fluctuation analysis (DFA) estimator which was previously proved to be among the best estimators which are not wavelet-based estimators. The WTMM estimator turns out to be a very competitive estimator which can be further generalized to characterize multiscaling behavior     

基于EGARCH过程的电磁频谱占用状态波动特性分析

针对传统频谱占用度自回归移动平均(ARMA)模型由于未考虑序列的条件二阶矩,导致无法准确描述频谱占用状态的非线性时变特性问题,该文提出一种基于指数广义自回归条件异方差(EGARCH)过程的频谱占用状态时间序列建模方法。首先通过对ARMA模型的剩余残差进行条件异方差性检验,表明频谱占用时间序列存在明显的时域波动集聚性;其次基于EGARCH过程构建频谱占用度时间序列模型以及对实测数据的分析,表明该模型相较ARMA模型对频谱占用度的拟合与预测精度更高;最后由EGARCH模型参数存在杠杆效应系数,表明频谱占用状态变化对电磁环境波动的影响具有非对称性。研究结果表明EGARCH模型能够量化反映频谱占用状态的复杂非线性时变过程。     

网络流量预测中的时间序列模型比较研究

网络流量预测在新一代网络协议设计、网络管理与诊断、设计高性能路由器等方面都具有重要意义.目前通常采用ARMA和FARIMA时序模型对网络流量序列进行拟合与预测,但没有对时间尺度的大小与模型选择的关系进行研究.本文对实际网络流量在不同时间尺度(毫秒、秒、分)下进行了流量预测建模并对预测性能进行比较,分析表明使用时序模型进行流量预测时,大时间尺度(分)流量预测较小时间尺度(毫秒、秒)具有更小的预测误差.并且,对于小时间尺度上的自相似流量序列,自相似模型FARIMA并没有较其他时序模型有更好的预测性能.     

ARCH-Based Traffic Forecasting and Dynamic Bandwidth Provisioning for Periodically Measured Nonstationary Traffic

Network providers are often interested in providing dynamically provisioned bandwidth to customers based on periodically measured nonstationary traffic while meeting service level agreements (SLAs). In this paper, we propose a dynamic bandwidth provisioning framework for such a situation. In order to have a good sense of nonstationary periodically measured traffic data, measurements were first collected over a period of three weeks excluding the weekends in three different months from an Internet access link. To characterize the traffic data rate dynamics of these data sets, we develop a seasonal autoregressive conditional heteroskedasticity (ARCH) based model with the innovation process (disturbances) generalized to the class of heavy-tailed distributions. We observed a strong empirical evidence for the proposed model. Based on the ARCH-model, we present a probability-hop forecasting algorithm, an augmented forecast mechanism using the confidence-bounds of the mean forecast value from the conditional forecast distribution. For bandwidth estimation, we present different bandwidth provisioning schemes that allocate or deallocate the bandwidth based on the traffic forecast generated by our forecasting algorithm. These provisioning schemes are developed to allow trade off between the underprovisioning and the utilization, while addressing the overhead cost of updating bandwidth. Based on extensive studies with three different data sets, we have found that our approach provides a robust dynamic bandwidth provisioning framework for real-world periodically measured nonstationary traffic.     

A FARIMA-based technique for oil slick and low-wind areas discrimination in sea SAR imagery

This paper introduces a new analysis technique, using the fractionally integrated autoregressive-moving average (FARIMA) model, to distinguish between low-wind and oil slick areas in high-resolution sea synthetic aperture radar (SAR) imagery. The method deals with the estimation of the fractional differencing and autoregressive-moving average parameters of the mean radial power spectral density of sea SAR images. The algorithm is applied and validated on dark areas corresponding to oil slicks, oil spills, and low-wind sea surface anomalies in European Remote Sensing 1 and 2 Precision Images of the Mediterranean Sea, North Sea, and Atlantic Ocean.     

Identification and tuning fractional order proportional integral controllers for time delayed systems with a fractional pole

First order plus time delay model is widely used to model systems with S-shaped reaction curve. Its generalized form is the model with a single fractional pole replacing the integer order pole, which is believed to better characterize the reaction curve. In this paper, using time delayed system model with a fractional pole as the starting point, fractional order controllers design for this class of fractional order systems is investigated. Integer order PID and fractional order PI and [PI] controllers are designed and compared for these class of systems. The simulation comparison between PID controller and fractional order PI and [PI] controllers show the advantages of the properly designed fractional order controllers. Experimental results on a heat flow platform are presented to validate the proposed design method in this paper.     

An autoregressive model for analysis of ice sheet elevation change time series

We present an autoregressive (AR) model that can effectively characterize both seasonal and interannual variations in ice sheet elevation change time series constructed from satellite radar or laser altimeter data. The AR model can be used in conjunction with weighted least squares regression to accurately estimate any longer term linear trend present in the cyclically varying elevation change time series. This approach is robust in that it can account for seasonal and interannual elevation change variations, missing points in the time series, signal aperiodicity, time series heteroscedasticity, and time series with a noninteger number of yearly cycles. In addition, we derive a theoretically valid estimate of the uncertainty (standard error) in the long-term linear trend. Monte Carlo simulations were conducted that closely emulated actual characteristics of five-year elevation change time series from Antarctica. The Monte Carlo results indicate that the autoregressive approach yields long-term linear trends that are less biased than two other approaches that have been recently used for analysis of ice sheet elevation change time series. In addition, the simulation results demonstrate that the variability (uncertainty) of the long-term linear trend estimates from the AR approach is in very good agreement with the derived theoretical standard error estimates.     

Fade Statistics of Wireless Multi-User Systems

In this paper, we propose a new model named effective fade duration envelope to characterize the accumulative conditional fade durations of individual users or groups of users in wireless communication systems. The proposed model has the following novelties: (1) it introduces the statistical upper and lower bounds with the required degree of confidence for accumulative conditional fade durations during any given time interval; (2) it characterizes various conditional fading circumstances in wireless multi-user communication systems.     

基于广义合成分析和深度神经网络的自回归系数估计方法

自回归(AR)模型是一类描述时序序列相关性的有效方法,经典的AR系数估计方法对残差信号做了简单的假设,在噪声干扰等复杂场景中难以准确估计AR系数,而基于深度神经网络(DNN)的AR(DNN-AR)系数估计方法在训练中容易受到莱文逊-杜宾迭代(LDR)解法的数值稳定性的影响.为改善DNN-AR系数训练的稳定性和整体性能,在保证系统稳定性的前提下,本文利用精度转化提高系统运算速度的思路,提出了基于广义合成分析(GABS)模型的深度网络结构改善方法,提高了AR系数在含噪环境下估计的准确性和网络训练的稳定性.组合DNN的GABS(GABS-DNN)的模型由三个主要部分组成:修正器的谱增强网络、编码器的DNN预处理及LDR参数估计和解码器的AR系数到功率谱的转换.在优化目标函数的过程中,引入了增强谱和观测谱的误差,减少了反向传播时LDR的梯度对增强网络的影响,实现了稳定估计含噪语音的AR系数.     

A statistical mechanical analysis of postural sway using non-Gaussian FARIMA stochastic models

In this paper, postural sway is modeled using a fractional autoregressive integrated moving average (FARIMA) family of models: the center-of-pressure (COP) motion is viewed in terms of a self-similar, anti-persistent random-walk process, obtained by fractionally summating non-Gaussian random variables, whose correlation structure for small time lags is shaped by a linear time-invariant low-pass filter. The model parameters are: the strength of the stochastic driving, e.g., the root mean square (rms) value of the time-difference COP motion; the DC gain, damping ratio and natural frequency of the filter; the Hurst exponent, which measures the random-walk antipersistence magnitude. In the proposed modeling procedure, a graphical estimator for determining the Hurst exponent is cascaded to a method for matching autoregressive (AR) models to fractionally difference COP motion via higher order cumulants. The effect of the presence or absence of vision on the model parameter values is discussed with regard to data from experiments on healthy young adults.     

BP神经网络在元器件非工作可靠性参数预测中的应用

应用神经网络模型对某电子元器件在非工作状态时的可靠性性能参数时间序列进行预测,并与自回归模型预测结果进行了比较.检验结果表明,神经网络预测模型有较高的精度,具有很好的实用价值.     

网络视频业务流有效带宽的计算

随着网络业务种类的丰富及服务质量要求的提高,如何有效地分配有限的资源显得越来越重要。本文针对三种常见的业务流模型,分析了对视频业务流的带宽的计算问题,提出了带宽计算方法,并进行了仿真。仿真结果表明,在缓冲区大小固定的条件下,视频业务流的带宽计算采用对数正态-FARIMA模型可以获得较小溢出率。