Parameter Estimation of Time-Varying ARMA Model

The auto-regressive moving-average (ARMA) model with time-varying parameters is analyzed. The time-varying parameters are assumed to be a linear combination of a set of basis time-varying functions, and the feedback linear estimation algorithm is used to estimate the time-varying parameters of the ARMA model. This algorithm includes 2 linear least squares estimations and a linear filter. The influence of the order of basis time-(varying) functions on parameters estimation is analyzed. The method has the advantage of simple, saving computation time and storage space. Theoretical analysis and experimental results show the validity of this method.

SAMPLED-DATA STATE ESTIMATION FOR NEURAL NETWORKS WITH ADDITIVE TIME–VARYING DELAYS

In this paper, we consider the problem of delay-dependent stability for state estimation of neural networks with two additive time–varying delay components via sampleddata control. By constructing a suitable Lyapunov–Krasovskii functional with triple and four integral terms and by using Jensen's inequality, a new delay-dependent stability criterion is derived in terms of linear matrix inequalities(LMIs) to ensure the asymptotic stability of the equilibrium point of the considered neural networks. Instead of the continuous measurement,the sampled measurement is used to estimate the neuron states, and a sampled-data estimator is constructed. Due to the delay-dependent method, a significant source of conservativeness that could be further reduced lies in the calculation of the time-derivative of the Lyapunov functional. The relationship between the time-varying delay and its upper bound is taken into account when estimating the upper bound of the derivative of Lyapunov functional. As a result, some less conservative stability criteria are established for systems with two successive delay components. Finally, numerical example is given to show the superiority of proposed method.

Static Frame Model Validation with Small Samples Solution Using Improved Kernel Density Estimation and Confidence Level Method

An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.

Bayesian Estimation of Population Size via Capture-Recapture Model with Time Variation and Behavioral Response

We consider the problem of population estimation using capture-recapture data, where capture probabilities can vary between sampling occasions and behavioural responses. The original model is not identifiable without further restrictions. The novelty of this article is to expand the current research practice by developing a hierarchical Bayesian approach with the assumption that the odds of recapture bears a constant relationship to the odds of initial capture. A real-data example of deer mice population is given to illustrate the proposed method. Three simulation studies are developed to inspect the performance of the proposed Bayesian estimates. Compared with the maximum likelihood estimates discussed in Chao et al. (2000), the hierarchical Bayesian estimate provides reasonably better population estimation with less mean square error;moreover, it is sturdy to underline relationship between the initial and re-capture probabilities. The sensitivity study shows that the proposed Bayesian approach is robust to the choice of hyper-parameters. The third simulation study reveals that both relative bias and relative RMSE approach zero as population size increases. A R-package is developed and used in both data example and simulation.

A Comparison of Hierarchical Bayesian Models for Small Area Estimation of Counts

Small area estimation (SAE) tackles the problem of providing reliable estimates for small areas, i.e., subsets of the population for which sample information is not sufficient to warrant the use of a direct estimator. Hierarchical Bayesian approach to SAE problems offers several advantages over traditional SAE models including the ability of appropriately accounting for the type of surveyed variable. In this paper, a number of model specifications for estimating small area counts are discussed and their relative merits are illustrated. We conducted a simulation study by reproducing in a simplified form the Italian Labour Force Survey and taking the Local Labor Markets as target areas. Simulated data were generated by assuming population characteristics of interest as well as survey sampling design as known. In one set of experiments, numbers of employment/unemployment from census data were utilized, in others population characteristics were varied. Results show persistent model failures for some standard Fay-Herriot specifications and for generalized linear Poisson models with (log-)normal sampling stage, whilst either unmatched or nonnormal sampling stage models get the best performance in terms of bias, accuracy and reliability. Though, the study also found that any model noticeably improves on its performance by letting sampling variances be stochastically determined rather than assumed as known as is the general practice. Moreover, we address the issue of model determination to point out limits and possible deceptions of commonly used criteria for model selection and checking in SAE context.

Parameter Estimation of Varying Coefficients Structural EV Model with Time Series

In this paper, the parameters of a p-dimensional linear structural EV(error-in-variable)model are estimated when the coefficients vary with a real variable and the model error is time series.The adjust weighted least squares(AWLS) method is used to estimate the parameters. It is shown that the estimators are weakly consistent and asymptotically normal, and the optimal convergence rate is also obtained. Simulations study are undertaken to illustrate our AWLSEs have good performance.

Sample-data Decentralized Reliable H∞ Hyperbolic Control for Uncertain Fuzzy Large-scale Systems with Time-varying Delay

这份报纸学习样品数据的问题为有变化时间的延期的不明确的连续时间的模糊大规模系统的可靠 H 夸张控制。第一，模糊夸张模型( FHM )被用来为某些复杂大规模系统建立模型，然后根据 Lyapunov 指导方法和大规模系统的分散的控制理论，线性 matrixine 质量( LMI )基于条件 arederived toguarantee H 性能不仅当所有控制部件正在操作很好时，而且面对一些可能的致动器失败。而且，致动器的精确失败参数没被要求，并且要求仅仅是失败参数的更低、上面的界限。条件依赖于时间延期的上面的界限，并且不依赖于变化时间的延期的衍生物。因此，获得的结果是不太保守的。最后，二个例子被提供说明设计过程和它的有效性。

Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.

A Simulation Study on Comparing General Class of Semiparametric Transformation Models for Survival Outcome with Time-Varying Coefficients and Covariates

The consideration of the time-varying covariate and time-varying coefficient effect in survival models are plausible and robust techniques. Such kind of analysis can be carried out with a general class of semiparametric transformation models. The aim of this article is to develop modified estimating equations under semiparametric transformation models of survival time with time-varying coefficient effect and time-varying continuous covariates. For this, it is important to organize the data in a counting process style and transform the time with standard transformation classes which shall be applied in this article. In the situation when the effect of coefficient and covariates change over time, the widely used maximum likelihood estimation method becomes more complex and burdensome in estimating consistent estimates. To overcome this problem, alternatively, the modified estimating equations were applied to estimate the unknown parameters and unspecified monotone transformation functions. The estimating equations were modified to incorporate the time-varying effect in both coefficient and covariates. The performance of the proposed methods is tested through a simulation study. To sum up the study, the effect of possibly time-varying covariates and time-varying coefficients was evaluated in some special cases of semiparametric transformation models. Finally, the results have shown that the role of the time-varying covariate in the semiparametric transformation models was plausible and credible.

“双支柱”政策调控与企业金融资产配置时变关系研究:基于TVP-SV-VAR模型的实证检验

金融科技发展和市场制度变革给传统金融体系带来的巨大冲击,使得“双支柱”政策调控的有效性面临严峻考验。借助TVP-SV-VAR模型,实证检验“双支柱”政策调控对企业金融资产配置的动态脉冲效应。研究表明,货币政策和宏观审慎政策的调控效果呈现出阶段性的演化特征,二者的协同效应能够有效抑制企业金融资产的过度配置。相比制度环境冲击,“双支柱”政策调控在面对金融环境冲击时表现出更强的作用效果和力度。进一步研究发现,“双支柱”政策通过资本流动管理、资产价格稳定和金融风险缓释途径影响企业金融资产配置,且对流动性与非流动性金融资产配置的作用效果存在显著差异。研究结论为理解实体企业金融化和完善金融市场制度提供了新的理论视角,也为政府相关部门依据金融环境和制度环境进行“双支柱”政策调控的动态调整提供决策依据。

时变小样本条件下基于对比学习的故障诊断

时变工况下的故障诊断往往具有高度动态性,而小样本下模型学习受限使得问题更加棘手。针对上述情况,提出了基于对比深度卷积网络的故障诊断方法:首先,针对数据样本量小的特点,利用速度变化引起的振动数据分布差异,无需进行人工操作自然实现数据增强;然后,在数据处理过程中,采用不同转速下相同健康状态的振动数据作为正样本,同时将不同健康状态下的振动数据作为负样本,通过比较样本之间的相似度来提取关键特征,从而缩小正样本之间的距离,同时增大负样本之间的距离;最后采用对比训练方式进行训练优化,将对比损失和交叉熵损失加权组合作为综合损失函数,使模型在学习特征表示的同时能有效进行分类任务。将该方法分别应用于两种不同时变转速下轴承故障数据集进行案例研究。试验结果表明,所提模型不仅在特征提取和分类任务中表现优异,而且在数据匮乏和时变转速工况下均能实现高准确率的故障诊断。验证了所提模型在处理时变小样本数据方面表现出较高的可行性和有效性,且优于其他先进诊断方法。

锂离子电池的等效时变内阻模型及应用

针对锂(离子)电池在恒流、恒压和/或恒功率等工况下,Thevenin等效电路模型无法在线辨识其模型参数,进而无法描述锂电池特性的问题,提出了一种新的等效时变内阻模型。分析表明,通过将锂电池的开路电压和内阻,描述成未知的时变电压和内阻,并利用安时积分和随机漫步模型,分别表示其未知时变电压和内阻的进化,就可为锂电池建立一种以状态空间进行描述的等效时变内阻模型。分析也表明,因电池充放电而产生的暂态极化电压,可由电池端电流与时变内阻的乘积,以及电池端电压观测噪声分布的组合进行描述。本文模型相比传统模型,在恒流恒压以及恒功率的工况下,SoC估计的均方根误差平均降低了48%。公开充放电测试数据集和实验的SoC估计,均验证了本文模型及分析结果的正确性和有效性。

面向时变机动目标的自适应航迹整体估计方法

面对目标运动模型时变未知的复杂机动目标跟踪场景,采用确定性的状态空间模型会因模型失配而导致跟踪性能显著下降.为此,提出一种基于航迹整体估计的自适应跟踪方法.该方法将目标航迹整体建模为多项式时间序列,建立时间多项式模型表征其运动状态的演化过程,依据最小离差平方和准则对多项式系数进行矩阵形式的递归估计,从而在每个采样时刻实现对全时段航迹的整体估计,并进一步提出多项式系数的渐进解耦估计方法以降低运算复杂度.在此基础上,采用不同次数的多项式模型构建多通道的并行航迹整体估计器组,根据实时误差分析结果进行自适应通道选择,以应对时变机动目标复杂运动形式引起的单一模型失配问题.仿真结果表明,该方法在无法确知目标真实运动状态的情况下,能够实现自适应选择跟踪通道及模型参数调整,相较传统递推滤波方法,该整体估计方法具有更好的跟踪性能.

基于Kriging模型的大跨度拱桥时变地震易损性分析

文章以桥墩钢筋锈蚀机理为基础,探究了基于氯离子环境侵蚀引起的钢筋直径及力学性能的退化规律,同时考虑地震动和结构参数的变异性,因分析计算量很大,引入Kriging模型和拉丁超立方抽样(Latin hypercube sampling,LHS)法建立易损性曲线,并利用Monte Carlo抽样方法形成全寿命周期内的桥梁系统损伤概率曲面。运用OpenSEES程序引入时变效应,对某大跨度拱桥建立有限元模型。分析结果表明:Kriging模型能够较精确地代替有限元进行分析,能够显著减小计算量;对于大跨度拱桥而言,由Monte Carlo抽样得到的全桥体系易损性来评价桥梁的抗震性能更加合理;在全寿命设计基准期内,随着服役时间增加,桥墩损伤概率明显增大。该研究可为类似桥梁结构全寿命抗震性能设计提供参考。

基于混频数据抽样的已实现EGARCH模型的波动率预测

该文以沪深300股指期货高频数据为样本,在Realized EGARCH模型的基础上引入了混频数据抽样(MIDAS)结构与时变波动,构建了基于偏t分布的SMA-Realized EGARCH MIDAS模型,该模型提高了模型捕捉长记忆性的能力,更好地刻画了模型的时变波动性.通过滚动时间窗的方法对模型进行VaR预测与后验测试,采用MCS检验评估各模型在不同测度下的波动率预测能力.研究结果显示:相比于传统的Realized GARCH模型、Realized EGARCH模型和Realized EGARCH MIDAS模型,本文提出的SMA-Realized EGARCH MIDAS模型具有更好的样本拟合效果与样本外波动率预测精度.

基于有偏误辅助变量的分层贝叶斯小域估计方法研究

抽样调查中的小域估计问题指的是,根据较少样本量进行一定精度下子总体估计的现实问题。与基于设计的方法不同,基于模型的方法不依赖大样本理论,能在估计过程中借助其他域的样本信息,更加适用于小域估计问题。然而,现实中测量误差无法完全避免,当模型协变量有偏误时,小域估计结果失效。对此,采用测量误差模型校正辅助变量误差,基于单元层次的分层贝叶斯模型进行小域估计,并在贝叶斯框架下估计辅助变量偏误机制。鉴于实际调查中为方便数据编码与统计、控制无回答误差,调查结果以分类型数据居多,本文重点讨论了更适用于小域估计问题的模型方法,针对分类型辅助变量存在测量误差的情形,给出了方法合理性的证明,同时通过模拟和实证对其估计效果进行验证与实践。本文模拟六种实践中常见的情形,除仅有分类型变量存在测量误差的情形之外,还考虑了存在测量误差的变量既有分类型又有连续型的情形等。数值模拟与实证结果一致表明,本文方法不仅能充分纳入与推断相关的不确定性因素,克服样本量受限的问题,还具有广泛的适用性,相较于传统方法,估计结果在提升准确度的同时更为稳健。

基于BP神经网络的小样本失效数据下继电保护可靠性评估

失效数据样本过少会影响对高可靠性继电保护系统的可靠性评估,因此提出一种基于BP神经网络的继电保护系统可靠性评估方法。分析了可用于继电保护装置可靠性评估的分布模型及其特点;利用原始小样本失效数据训练BP神经网络,得到与原始数据样本规律相近的扩充数据样本;利用最小二乘法对扩充数据样本的分布模型进行参数估计。算例分析表明:利用扩充数据样本进行可靠性评估效果更好,在对继电保护装置进行可靠性评估时应根据选择的分布模型选择合适的经验公式。

基于TVAR-HMM的滚动轴承故障诊断

针对工况条件下轴承故障振动信号的非平稳特性,分析时变自回归与隐马尔科夫模型的特点,提出了一种基于时变自回归和隐马尔科夫模型的滚动轴承故障诊断方法.振动信号经时变自回归建模后,得到时频分辨率较高、无交叉干扰项的时频谱,基于能量法对时频谱进行特征提取,然后利用隐马尔科夫模型对故障特征统计分类,实现对轴承故障的诊断.轴承信号分析表明,TVAR建模可以有效地提取信号中的故障特征,结合隐马尔科夫模型的动态统计特性可智能识别轴承故障类型,得到良好的诊断效果.

基于时变相量小信号模型的逆变器并联控制系统分析与设计

在逆变器无线并联系统中,下垂控制器的参数会影响功率均分的动态响应和稳定性。传统的方法是利用准稳态相量的小信号模型来辅助设计下垂控制器。然而,交流信号的准稳态相量在分析动态响应较快的逆变器并联系统时会受到局限。以使用下垂控制的400 Hz逆变器并联系统为研究对象,建立该系统基于时变相量的小信号模型。理论分析表明,传统的小信号模型在设计并联系统参数时有局限性。与传统的模型相比,所建立的基于时变相量的小信号模型考虑了下垂调节的暂态过程,解释了传统模型扩大了系统参数选择范围的原因,并对并联系统的参数提供了更加精确的选择范围。仿真和实验结果验证了该小信号模型的准确性。