废气氧传感器Hammerstein模型结构的确定

研究了废气氧(EGO)传感器Hammerstein模型结构辨识方法。静态非线性函数选用双曲正切与多项式组合形式,动态线性环节分别选用带外生变量的自回归(ARX)模型、输出误差(OE)模型和Box-Jenkins(BJ)模型结构。采用交叉准则法进行参数估计和阶次选择,通过仿真比较对模型进行检验。结果表明:最终输出误差(FOE)准则和最终预报误差(FPE)准则均适用于用估计数据选择阶次,但前者比后者更可靠。基于预测误差法的3阶OE模型和BJ模型均可用于EGO传感器Hammerstein模型动态线性环节的建模。     

基于小波去噪和AR建模的MEMS陀螺数据处理方法

为解决MEMS陀螺输出信号中噪声大、随机漂移严重的问题,提出了一种基于小波去噪和AR建模的MEMS陀螺组合数据处理方法.采用小波去噪法对MEMS陀螺输出信号去噪,自适应确定小波分解层数,提高了其信噪比.采用AR(autoregressive,自回归)模型对MEMS陀螺的随机漂移进行建模,利用平均均方预测误差确定模型的最佳阶数,并与传统的一阶马尔可夫模型进行了比较.实验结果表明,该组合数据处理方法可有效抑制MEMS陀螺输出噪声,且能更精确地对MEMS陀螺随机漂移进行建模及预测.     

自回归模型参数的递阶辨识

本文给出了自回归模型系数与误差方差的递阶估计算法,导出了AIC(p)定阶准则在工程中易于实现的数学关系式,并进行了仿真计算.     

MEMS加速度计噪声分析与降噪方法研究

针对微机电系统(MEMS)加速度计的随机噪声对输出信号干扰的情况,提出了对加速度计噪声源及噪声类型进行辨识、估计与建模,并确定误差补偿的降噪方法,以提高加速度计精度.采用Allan方差分析法对MEMS加速度计的随机噪声进行分析,得到了影响MEMS加速度计性能的几种主要随机噪声,使用自回归滑动平均模型(ARMA)对加速度计输出数据进行数学建模,以最终预测误差(FPE)准则确定使用的模型与阶次.设计了Kalman滤波算法,对加速度计进行降噪,通过Allan方差方法对Kalman算法滤波效果进行分析.实验结果表明:Kalman滤波能有效降低加速度计的随机噪声.     

AR模型在人口增长预测中的应用

采用AIC信息准则来确定AR模型的阶.模型参数估计使用了方法简单、参数估计无偏、精度高的最小二乘法.理论分析和仿真结果均表明,AR模型能够较好地解决人口增长预测这一非线性问题,并取得一定的预测精度.     

基于OIVPM的特征值确定ARMA模型的结构

基于最小描述长度(MDL)准则,提出了一种新的自回归滑动平均(ARMA)模型结构辨识方法.该方法将ARMA模型的结构辨识分两步进行:首先利用超定辅助变量乘积矩(0IVPM)的最小特征值确定自回归(AR)部分的阶,然后利用协方差矩阵的特征值估计滑动平均(MA)部分的阶.方法的可行性与有效性通过大量的数值仿真得到验证.     

一种新的统计预测模型——多项式系数自回归模型

传统的自回归滑动平均模型（ARMA）和新近出现的函数系数自回归模型（FAR）不能满足非线性时间序列预测分析的准确度与运算速度要求,为了改进预测性能,研究提出了一种新的统计预测模型——多项式系数自回归模型（PCAR）。给出了PCAR模型的表示形式,详细探讨了PCAR模型的参数估计和阶次选择方法,在此基础上又提出了基于BIC准则的建模算法。同ARMA模型相比,PCAR模型扩大了适用对象范围,有效降低了模型选择误差;同FAR模型相比,它具有参数模型的特点,避免了系数函数局部线性回归估计所存在的不足;分析了PCAR模型与ARMA、FAR模型的等价条件。通过实验分析得出了PCAR模型较ARMA、FAR模型的单步预测准确度分别提高了99.65%和18.7%的结论,而且PCAR建模运算所需时间仅为FAR模型的0.2%。     

Kalman滤波在Yule-Walker谱估计中的应用

在现代谱估计中,由于Yule-Walker沃克谱估计算法中平稳随机序列的长度n在某些情况下偏小,使计算出的ARMA随机过程的功率谱密度不能精确逼近真实值,所以我们在用自相关法估计AR模型参数时加入了kalman滤波器。将估计的AR模型系数及高斯白噪声作为滤波器的输入及部分参数,对最终估计的功率谱进行修正。实验结果表明,...     

自回归滤波器的设计

介绍了滤波器设计中近代谱估计法的基本思想，重点分析了自回归（AR）模型、以及模型参数与自相关函数、功率谱之间的关系。通过Levinson—Durbin递推算法来确定自回归模型的系数，并给出AR模型阶数的算法。用递推公式得到滤波器的输出。经过分析得出结论：自回归模型的参数估计计算简单，具有递推特性，适合表示很窄的频谱。     

改进的Burg最大熵法在管道检测中的应用

短时间序列、高分辨率、强抗噪能力的功率谱估计是管道超声内检测的关键技术.针对Burg最大熵法存在的问题,从减小递推算法初始阶段误差出发,提出二阶预测误差滤波器系数倒推法,由二阶滤波器系数修正一阶反射系数,保证递推初始阶段最大熵原则,以适应短时间序列谱估计,算法在管道内检测实验中得到了较好的结果.     

Bayesian-like autoregressive spectrum estimation in the case of unknown process order

Initially, the problem of estimation of the spectral density function of a stationary multivariate autoregressive Gaussian process of unknown order is considered. Two new solutions to this problem are presented. The proposed estimators, suggested by the form of the Bayesian predictor in autoregressive systems, can be characterized as the average model spectrum and the spectrum corresponding to the "averaged model," with the averages being computed over the set of competitive autoregressive models of different orders and with respect to the sequence of the posterior probabilities of the process order given its observation history. The obtained results are then extended to the case of nonstationary autoregressive processes (identified by means of the exponentially weighted estimators) and more general weighting sequences. Although not Bayesian in the strict sense, the proposed approaches seem to be interesting from the theoretical point of view and give better results than the "classical" one. The efficient computational algorithms are indicated and the results of computer simulations are discussed.     

牛顿法在CELP零极点模型改进方法中的应用

传统的码激励线性预测(CodeExcitedLinearPredictive,CELP)语音编码方法采用有限阶的全极点预测模型来描述语音信号的短时相关性,对频谱谷点显著的语音信号(如清音等)并不适合。论文针对传统CELP编码器的这个缺点,尝试采用零极点预测模型对传统CELP编码器中的短时预测进行改进,并提出了一种基于牛顿法的零极点模型参数估计方法。实验结果表明,论文所提的方法可以有效地减少CELP编码器合成语音的归一化均方误差,提高合成语音的质量。     

基于数据驱动多输出ARMAX建模的高炉十字测温中心温度在线估计

高炉（Blast furnace,BF）炼铁中,十字测温作为炉顶温度和煤气流分布监测的最主要手段,对高炉的安全、稳定和高效运行起着重要作用.然而,由于高炉炉顶中心部位温度较高,造成十字测温装置中心位置传感器极易损坏,并且更换周期长,因而无法及时判断炉顶煤气流分布.针对这一实际工程问题,本文基于时间序列建模思想,集成采用多输出自回归移动平均（Multi-output autoregressive moving average,M-ARMAX）建模、因子分析、Pearson相关分析、基于赤池信息准则（Akaike information criterion,AIC）与模型拟合优度联合定阶等混合技术,提出一种模型结构简单、精度较高且易于工程实现的十字测温中心温度在线估计方法.首先,提出利用因子分析与Pearson相关分析相结合的稳健特征选择方法选取多输出建模输入变量.然后,采用样本均值消去法预处理采集的高炉样本数据,使其成为离散随机数.基于离散随机数,建立算法简单、易于工程实现的M-ARMAX温度模型：为了克服传统基于AIC阶数确定造成模型阶次高、结构复杂的问题,提出在AIC准则基础上进一步引入模型拟合优度来选取模型最小阶,可保证模型估计精度的同时降低模型阶次;同时,采用可快速收敛的递推最小二乘算法辨识M-ARMAX模型参数,并用残差分析方法检验模型.工业试验和比较分析表明：建立的M-ARMAX模型能够根据实时数据同时对十字测温装置多个中心温度点进行准确和稳定估计,且模型估计误差符合高斯白噪声特性.     

A genetic algorithm approach to the spectral estimation of time series with noise and missed observations

This study considers the problem of estimating the autoregressive moving average (ARMA) power spectral density when measurements are corrupted by noise and by missed observations. The missed observations model is based on a probabilistic structure. Unlike conventional cases of missed observation in parameter estimation problems, the variance of noise is unavailable, that is the time points of missed observations are unknown, and the probability of missing data needs to be estimated. In this situation, spectral estimation is more difficult to solve and becomes a highly nonlinear optimization problem with many local minima. In this paper, we use the genetic algorithm (GA) method to achieve a global optimal solution with a fast convergence rate for this spectral estimation problem. From the simulation results, we have determined that the performance is significantly improved if the probability of data loss is considered in the spectral estimation problem.     

Variational Bayesian learning for robust AR modeling with the presence of sparse impulse noise

A single distribution is typically used to model the innovations of an autoregressive (AR) model. However, sparse impulses may exist within the innovations which may cause outliers in the observations. These impulses cannot be modeled by a single distribution and may potentially degrade the estimation. In this study, the innovation of an AR model is modeled by using both a Gaussian noise component and a sparse impulse noise model in order to obtain robust estimation and estimation of the impulses simultaneously. The Gaussian distribution models the normal noise and the sparse impulse noise model models the sparse abnormal innovation impulses. A hierarchal Bayesian model is built for the proposed model. Automatic relevance determination (ARD) priors are set for both the coefficients and the sparse impulses. A Variational Bayesian (VB) learning algorithm is given to estimate the parameters of the model. Experimental results show that the proposed model with the learning algorithm is valid for AR models with outliers caused by sparse innovation impulses, the coefficient estimation accuracy is better than other methods, and the sparse impulses can be estimated simultaneously.     

A smoothness priors long AR model method for spectral estimation

A new smoothness priors long AR model method approach is taken to the short data span spectral estimation problem. An autoregressive (AR) model that is relatively long compared to the data length is considered. The smoothness priors are in the form of the integrated squared derivatives of the AR model whitening filter. A smoothness tradeoff parameter or Bayesian hyperparameter balances the tradeoff between the infidelity of the AR model to the data and the infidelity of the model to the smoothness constraint. The critical computation of the likelihood of the hyperparameters of the Bayesian model is realized by a constrained least squares computation. Numerical examples are shown. The results of simulation studies using entropy comparison evaluations of the Bayesian and minimum AIC-AR methods of spectral estimation are also shown.     

On the application of the Wald statistic to order estimation ofARMA models

Consistent criteria for order estimation of autoregressive moving-average (ARMA) processes based on the Wald statistic are presented. The new criteria require only the estimation of the model parameters at the largest order, unlike alternative methods in the literature that require the estimation of the model parameters at all possible orders     

Spectral domain estimation of autoregressive moving-average models using shuttle iterative methods

An efficient iterative algorithm for the estimation of autoregressive moving-average (ARMA) time series models is presented. The prediction error criterion is transformed into the spectral domain, and the autoregressive (AR) parameters are computed (inner loop) using the Newton iterative steps by fixing the AR parameters. Use of data compression in the form of signal power spectrum (fewer frequency points compared to large data size), application of linear solution methods, and the ease of closed form computation of gradients and Hessian matrix of the optimality criterion result in an efficient estimation algorithm for systems with varied spectral forms. The algorithm is implemented on a PDP 11/35 mini computer, and may be used for on-line monitoring and diagnostics of dynamic processes (nuclear power reactors, chemical industry processes), pattern recognition systems, and evaluation of sensor response time characteristics.     

Color spectral analysis for spatial structure characterization of textures in IHLS color space

In this article, a linear prediction model based approach for color texture characterization and classification in the improved hue luminance and saturation color space is presented. Pure chrominance structure information is used in addition to the normally used luminance structure information for color texture classification. Hue and saturation channels of a color image in IHLS color space are combined using a complex exponential to give a single channel which holds all the chrominance information of the image. Two dimensional complex multichannel versions of the non-symmetric half plane autoregressive model, the quarter plane autoregressive model and the Gauss Markov random field model are used to perform parametric power spectrum estimation of both luminance and the “combined chrominance” channels of the image. The accuracy and precision of these spectral estimates are proven quantitatively by performing tests on a large number of images. Spectral distance measures are calculated for the spectral information of luminance and chrominance channels individually as well as combined through a combination coefficient. Using these distance measures, color texture classification is done with k-nearest neighbor algorithm. Experimental results verify that the IHLS color space exhibits better performance than the RGB color space indicating the significance of using IHLS for such analysis. They also show that color texture characterization and percentage classification obtained by combined luminance and chrominance structure information is better than the color texture classification done using only the luminance structure information.     

Frequency-domain identification of continuous-time ARMA models from sampled data

The subject of this paper is the direct identification of continuous-time autoregressive moving average (CARMA) models. The topic is viewed from the frequency domain perspective which then turns the reconstruction of the continuous-time power spectral density (CT-PSD) into a key issue. The first part of the paper therefore concerns the approximate estimation of the CT-PSD from uniformly sampled data under the assumption that the model has a certain relative degree. The approach has its point of origin in the frequency domain Whittle likelihood estimator. The discrete- or continuous-time spectral densities are estimated from equidistant samples of the output. For low sampling rates the discrete-time spectral density is modeled directly by its continuous-time spectral density using the Poisson summation formula. In the case of rapid sampling the continuous-time spectral density is estimated directly by modifying its discrete-time counterpart.