Partial Time-Varying Coefficient Regression and Autoregressive Mixed Model

Regression and autoregressive mixed models are classical models used to analyze the relationship between time series response variable and other covariates. The coefficients in traditional regression and autoregressive mixed models are constants. However, for complicated data, the coefficients of covariates may change with time. In this article, we propose a kind of partial time-varying coefficient regression and autoregressive mixed model and obtain the local weighted least-square estimators of coefficient functions by the local polynomial technique. The asymptotic normality properties of estimators are derived under regularity conditions, and simulation studies are conducted to empirically examine the finite-sample performances of the proposed estimators. Finally, we use real data about Lake Shasta inflow to illustrate the application of the proposed model.

Partial Time-Varying Coefficient Regression and Autoregressive Mixed Model

Regression and autoregressive mixed models are classical models used to analyze the relationship between time series response variable and other covariates. The coefficients in traditional regression and autoregressive mixed models are constants. However, for complicated data, the coefficients of covariates may change with time. In this article, we propose a kind of partial time-varying coefficient regression and autoregressive mixed model and obtain the local weighted least-square estimators of coefficient functions by the local polynomial technique. The asymptotic normality properties of estimators are derived under regularity conditions, and simulation studies are conducted to empirically examine the finite-sample performances of the proposed estimators. Finally, we use real data about Lake Shasta inflow to illustrate the application of the proposed model.

Prediction of earth rotation parameters based on improved weighted least squares and autoregressive model

In this paper, an improved weighted least squares （WLS）, together with autoregressive （AR） model, is proposed to improve prediction accuracy of earth rotation parameters（ERP）. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.

AUTOREGRESSIVE MODEL AND POWER SPECTRUM CHARATERISTICS OF CURRENT SIGNAL IN HIGH FREQUENCY GROUP PULSE MICRO-ELECTROCHEMICAL MACHINING

The identification of the inter-electrode gap size in the high frequency group pulse micro-electrochemical machining （HGPECM） is mainly discussed. The auto-regressive（AR） model of group pulse current flowing across the cathode and the anode are created under different situations with different processing parameters and inter-electrode gap size. The AR model based on the current signals indicates that the order of the AR model is obviously different relating to the different processing conditions and the inter-electrode gap size; Moreover, it is different about the stability of the dynamic system, i.e. the white noise response of the Green＇s function of the dynamic system is diverse. In addition, power spectrum method is used in the analysis of the dynamic time series about the current signals with different inter-electrode gap size, the results show that there exists a strongest power spectrum peak, characteristic power spectrum（CPS）, to the current signals related to the different inter-electrode gap size in the range of 0～5 kHz. Therefore, the CPS of current signals can implement the identification of the inter-electrode gap.

Modified switched IMM estimator based on autoregressive extended Viterbi method for maneuvering target tracking

In this paper, a new approach of maneuvering target tracking algorithm based on the autoregressive extended Viterbi(AREV) model is proposed. In contrast to weakness of traditional constant velocity(CV) and constant acceleration(CA) models to noise effect reduction, the autoregressive(AR) part of the new model which changes the structure of state space equations is proposed. Also using a dynamic form of the state transition matrix leads to improving the rate of convergence and decreasing the noise effects. Since AR will impose the load of overmodeling to the computations, the extended Viterbi(EV) method is incorporated to AR in two cases of EV1 and EV2. According to most probable paths in the interacting multiple model(IMM) during nonmaneuvering and maneuvering parts of estimation, EV1 and EV2 respectively can decrease load of overmodeling computations and improve the AR performance. This new method is coupled with proposed detection schemes for maneuver occurrence and termination as well as for switching initializations. Appropriate design parameter values are derived for the detection schemes of maneuver occurrences and terminations. Finally, simulations demonstrate that the performance of the proposed model is better than the other older linear and also nonlinear algorithms in constant velocity motions and also in various types of maneuvers.

PARTICLE FILTERING BASED AUTOREGRESSIVE CHANNEL PREDICTION MODEL

A particle filtering based AutoRegressive (AR) channel prediction model is presented for cognitive radio systems. Firstly, this paper introduces the particle filtering and the system model. Secondly, the AR model of order p is used to approximate the flat Rayleigh fading channels; its stability is discussed, and an algorithm for solving the AR model parameters is also given. Finally, an AR channel prediction model based on particle filtering and second-order AR model is presented. Simulation results show that the performance of the proposed AR channel prediction model based on particle filtering is better than that of Kalman filtering.

The First Order Autoregressive Model with Coefficient Contains Non-Negative Random Elements: Simulation and Esimation

This paper considered an autoregressive time series where the slope contains random components with non-negative values. The authors determine the stationary condition of the series to estimate its parameters by the quasi-maximum likelihood method. The authors also simulates and estimates the coefficients of the simulation chain. In this paper, we consider modeling and forecasting gold chain on the free market in Hanoi, Vietnam.

基于AR-ECM平均差异模型的串联电池组SOC、容量多尺度联合估计方法

考虑电池单体老化差异所致的电池组不一致性,针对串联电池组荷电状态(state of charge,SOC)、容量估计问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model,AR-ECM)的平均差异模型(mean-difference model,MDM)。基于此模型,提出串联电池组SOC、容量多尺度联合估计算法。该算法由2个部分组成,一是基于AR-ECM的MDM及差异化模型参数辨识策略:条件辨识策略和定频分组辨识策略;二是基于多时间尺度H无穷滤波(multi-timescale H infinity filter,Mts-HIF)的电池组SOC、容量联合估计算法。通过将所提出MDM中的自回归平均模型(autoregression mean model,AR-MM)与传统MDM中的n阶RC平均模型(nRC mean model,nRC-MM)比较,结果表明所提出的AR-MM在复杂运行工况下具有更优的动态跟随性能。依据最小化信息量准则(akaike information criterion,AIC),AR-MM具有更优的复杂度与精度的权衡。通过与基于多时间尺度扩展卡尔曼滤波(multi-timescale extended Kalman filter,Mts-EKF)联合状态估计算法比较,结果表明所提出的Mts-HIF状态估计算法具有更优的鲁棒性、精度和收敛速度。

Autoregressive trispectrum and its slices analysis of magnetorheological damping device

A combined magnetorheological damper combined with rubber spring and magnetorheological damper is addressed.This type of damping device has inherited the merits of rubber spring and the magnetorheological damper.The test damping device is made up of combined magnetorheological damper,amplitude controller,signal collecting device,computer software for dynamic analysis,etc.When a zeromean and non-Gaussian white noise interfere with the device,a time series autoregressive(AR) model is conducted by using the sampled experimental data.Trispectrum and its slices analysis are emerging as a new powerful technique in signal processing,which is put forward for investigating the dynamic characteristics of the magnetorheological vibrant device.The present of trispectrum and its slices analysis change with the variation of controllable working magnetic field of the damper correspondingly.It is indicated that AR trispectrum and its slices analysis methods are feasible and effective for investigation of magnetorheological vibrant device.

Differences of EEG between Eyes-Open and Eyes-Closed States Based on Autoregressive Method

Autoregressive （AR） power spectral density estimate method was used to analyze the electroencephalogram （EEG） signals in eyes-open and eyes-closed states. From the topographical distributions of delta, theta, alpha, and beta power spectrum, these two states can be clearly discriminated. In these two states, frontal areas were activated in delta power, both frontal and occipital areas were activated in theta band, and occipital areas were activated in alpha and beta bands. These four bands had significantly higher power in frontal, parietal, and occipital areas when eyes were close. The results also implied that the optimum order of AR model could be more suitable for estimating EEG power spectrum of different states.

Asymptotic Inference in the Random Coefficient Autoregressive Model with Time-functional Variance Noises

This paper considers the random coefficient autoregressive model with time-functional variance noises,hereafter the RCA-TFV model.We first establish the consistency and asymptotic normality of the conditional least squares estimator for the constant coefficient.The semiparametric least squares estimator for the variance of the random coefficient and the nonparametric estimator for the variance function are constructed,and their asymptotic results are reported.A simulation study is presented along with an analysis of real data to assess the performance of our method in finite samples.

基于JT-AR转换模型的非高斯风荷载特性分析

为了研究大跨度屋盖结构的非高斯风荷载特性,提出一种采用JT-AR转换模型模拟大跨度球面屋盖结构非高斯脉动风压的方法。基于JT变换和AR模型理论进行耦合,提出并构建JT-AR转换模型,模拟生成非高斯脉动风压时程样本数据,与目标功率谱及高阶统计量对比验证;通过已有风洞试验结果与作用在大跨度球面屋盖结构表面的非高斯分布特性作对比验证。结果表明:JT-AR转换模型的模拟结果与风洞试验作用在建筑上的非高斯脉动风具有同等作用效应,其模拟仿真结果具备可靠性及普适性。研究结论为大跨度结构抗风设计提供一种新的模拟方法,可代替复杂的风洞试验。

基于AR模型的上海地区地面沉降预测分析

利用AR模型,对上海地区地面沉降作了拟合评估,依据AIC推出了AR系列预报模型。计算结果表明,AR(4)模型的结果最符合真实值。用AR(4)模型对上海地区未来10年的地面沉降值进行了预测。

基于AR参数模型与聚类分析的肌电信号模式识别方法

肌电信号是与神经肌肉活动有关的生物电的体现,肌电信号的模式识别是肌电应用的基础。利用现代功率谱估计中的参数模型法,对从掌长肌、肱桡肌、尺侧腕屈肌和肱二头肌采集的4路表面肌电信号建立AR参数模型,并提取其AR模型参数作为信号的特征,构造特征矢量,提供给基于距离测度的Mahalanobis距离分类器进行模式分类,能够成功地识别出握拳、展拳、腕内旋、腕外旋、屈腕、伸腕、前臂内旋、前臂外旋8种动作模式。实验表明,该方法识别率高、鲁棒性好,为肌电等非平稳生物电信号的模式识别提供了一种新方法。

基于AR模型和支持向量机的故障诊断法

提出了一种基于时间序列AR模型和支持向量机的故障诊断方法。首先利用AR模型对振动信号进行建模,然后将AR模型自回归系数组成的特征向量输入到支持向量机,最后支持向量机完成对不同工况的分类识别。测试系统采用LabVIEW虚拟仪器构建,并通过计算机自动完成测试和分析。实验结果表明:这种基于时间序列AR模型和支持向量机的故障诊断方法是可行的。

基于AR和ARMA模型的多变量非高斯风压模拟

基于多变量非高斯随机过程间的相关性,将发展的单变量非高斯过程自回归和自回归滑动平均(AR和ARMA)模型模拟算法扩展至多变量非高斯过程的数值模拟。通过AR和ARMA模型系数考虑多变量非高斯过程间的相关性,建立多变量非高斯过程AR和ARMA模型的模拟算法。多变量非高斯风压的数值模拟表明:AR和ARMA模型算法能有效地模拟低斜度、中斜度和高斜度的多变量非高斯随机过程。

应用乘积季节ARIMA模型的话务量预测及结果分析

话务量预测功能对于电信网络规划建设、网络优化意义重大。深入研究了某省某移动网络运营商的多年的话务量数据,利用自相关函数对其周期性和趋势性方面的规律进行了探测,并在此基础上提出应用乘积季节ARIMA模型进行建模和预测的方案。进行了2008年7月到12月的全省及各地区月日均话务量的预测,并与网络实际运营结果进行了比较。所应用方法的一步预测值平均绝对百分比误差MAPE为1.382%,6步预测的MAPE值均在6%以内,是精确度很高的预测;对预测误差较大的某地区进行了原因分析,证明了模型的正确性,并为实际预测应用中经常遇到的预测误差偏大的问题提供了一种有效的分析思路和方法。

航空发动机性能参数联合RBFPN和FAR预测

排气温度是最能反映航空发动机运行状态的性能参数之一.对连续飞行班次的起飞排气温度裕度(EGTM,Exhaust Gas Temperature Margin)参数进行预测分析,有助于判知航空发动机将来的工作性能,为预防和排除故障提供充分的时间和决策依据.在依据具有非线性、非平稳特征的起飞EGTM历史监测值序列构建预测模型时,基于奇异值分解滤波算法提出了一种联合径向基函数预测网络(RBFPN,Radial Basis Function Prediction Networks)和函数系数自回归模型(FAR,Functional-coefficient Auto Regressive model)的预测方案,充分发挥RBFPN和FAR在预测EGTM参数值变动趋势成分和随机成分的各自优势,使其互为补充,协同处理.实验结果表明该联合预测方案能够有效抑制RBFPN或FAR单独采用时所呈现出的不足,提高预测性能.

小波分解AR-BP网络模型在大坝垂直位移预测中的应用

针对大坝内部垂直位移数据序列不仅具有周期性、平稳性,且在频域上存在高、低频,显著的多尺度等特性,本实验利用多尺度小波分析的原理与方法对数据序列进行分解,对低频序列和高频序列分别建立AR模型和BP神经网络模型并进行预测,最后叠加各个序列的预测值,得到数据序列的预测结果。该方法适用于大坝垂直位移的预测,结果与自回归模型及单BP神经网络模型相比,该模型具有更高预测精度。

大型旋转机械升降速过程故障诊断HMM-AR方法研究

由于大型旋转机械升降速过程振动的复杂多样性，对其进行监测能获得较平稳运行更多的机组状态信息。本文通过提取 Ａ Ｒ 系数反映振动特征，并将隐 Ｍ ａｒｋｏｖ 模型引入升降速过程故障诊断研究中，实验证明应用 Ｈ Ｍ Ｍ  Ａ Ｒ 法能较好地达到机组故障诊断目的。