非线性系统的多模态ARMAX模型及其参数辨识

本文利用非线性系统多个工作点上的线性化模型，建立了一类参数内插型多模态ＡＲＭＡＸ模型。与传统的线性化模型相比较，在更大的范围内有较高的精度，本文并给出了如何辨识多模态模型参数的方法，仿真例子表明了多模态模型及其参数辨识算法的有效性。     

基于辨识ARMA模型的野值剔除方法与卡尔曼滤波修正算法

本文基于ＡＲＭＡ模型，提出了一种新的野值剔除方法。文中首先建立了新息过程的ＡＲＭＡ模型，再应用递推增广最小二乘方法，在线辨识ＡＲＭＡ模型的参数，并通过模型参数变化的检验函数，来判定是否出现了野值。文中同时提出了野值剔除后卡尔曼滤波的修正算法。作为应用，我们对雷达半主动导引头寻的制导系统的野值情况进行了仿真。仿真结果表明，这种基于辨识ＡＲＭＡ模型的野值剔除方法与野值剔除后的卡尔曼滤波修正算法能有效地     

带白色观测噪声的ARMA模型参数的无偏估计

本文研究了如何利用受白色噪声污染的观测数据辨识ＡＲＭＡ（ｐ，ｑ）模型参数数据的问题，提出了一种递推辅助变量法．利用这种方法首先辨识出ＡＲ（ｐ）部分的参数及观测噪声的方差，然后根据所得的估计利用常用的Ｎｅｗｔｏｎ－Ｒａｐｈｓｏｎ方法确定ＭＡ（ｑ）部分的参数。     

利用双通道格形滤波器同时辨识ARMA模型的参数和阶次

本文重新构造了一种最小二乘通道格形滤波器，并将它用于同时辨识ＡＲＭＡ模型的参数和阶次。数值仿真结果表明，这种辨识算法具有计算量小、精度高等优点。     

多变量ARMA信号自校正最优去卷平滑估计方法及其渐近稳定性*

本文讨论信号去卷估计问题。运用新息理论和射影方法，基于ＡＲＭＡ新息模型设计多变量ＡＲＭＡ信号最优去卷平滑器，讨论了平滑器的渐近稳定性。在信号模型及噪声统计未知时，通过在线辨识ＡＲＭＡ新息模型提出了ＡＲＭＡ信号自校正去卷平滑器。     

采用加权控制律的自适应广义预测极点配置控制器

本文针对ＣＡＲＭＡ和ＣＡＲＩＭＡ模型，利用加权控制律代替一步预测控制律，通过在线选择加权项进行极点配置，避免了广义预测中的求逆问题，应用变遗忘因子最小二乘法辨识参数，仿真结果表明，该控制器适用于非最小相系统和开环不稳定系统，不但具有期望的动态特性，而且具有很好的鲁棒性。     

连续系统的模型参考自适应辨识新方法

１引言文［１］提出了将待辨系统以被控对象的形式嵌入到Ｎａｒｅｎｄｒａ等［２］的自适应控制结构中去的连续系统辨识方案，解决了传统的模型参考自适应辨识方法不能处理待辨系统不稳定或不具备良好暂态品质的问题．但它不能处理非最小相位系统，而且对于相对阶大于１并且传递函数分子阶次大于０的系统，因需要进行矩阵求逆而使运算复杂，精度降低．本文在文［１］的基础上，提出了将待辨系统与一个适当选择的相对阶为１的系统并联后作为被控对象嵌入到模型参考自适应控制系统（ＭＲＡＣＳ）中去的辨识方案，解决了上述问题。２辨识方案考…     

变阶式递推增广算法及应用

本文给出一种能自动改变模型阶数的变阶式递推增广的最小二乘法,在此基础上,对一类ＣＡＲＭＡ系统给出一种在线自动辨识机。实际应用表明,这种辨识机不仅可用于模型的在线辨识,大大减小建模的计算量,而且对系统进行预报时,具有较高的精度。     

自校正状态估计新算法

本文运用现代时间序列分析＾［１］的观点处理性离散时间系统自校正最优状态估计。运用新息理论和射影方法本文提出了一种新型最优滤器，在噪声统计未各时基于辨识ＡＲＭＡ新自模型提出了自校正滤波器，仿真例子说明新算法的效性。     

带有色观测噪声的多变量自校正去卷滤波器

本文运用新息理论和射影的方法，通过在线辨识ＡＲＭＲ新息模型提出了多变量非平稳ＡＲＭＡ信号自校正去卷滤波器。     

应用于地震数据去卷的自校正白噪声估值器

本文把地震数据去卷问题处理为估计带观测噪声的ARMA模型的白噪声问题,应用时间 序列分析方法提出了不同于Mendel的新的稳态最优白噪声估值器,文章基于两个ARMA新 息模型的在线辨识,进一步给出了自校正白噪声估值器.     

Identification of dynamic errors-in-variables models: Approaches based on two-dimensional ARMA modeling of the data

In this paper we propose a parametric and a non-parametric identification algorithm for dynamic errors-in-variables model. We show that the two-dimensional process composed of the input-output data admits a finite order ARMA representation. The non-parametric method uses the ARMA structure to compute a consistent estimate of the joint spectrum of the input and the output. A Frisch scheme is then employed to extract an estimate of the joint spectrum of the noise free input-output data, which in turn is used to estimate the transfer function of the system. The parametric method exploits the ARMA structure to give estimates of the system parameters. The performances of the algorithms are illustrated using the results obtained from a numerical simulation study.     

单输出系统最优自校正滤波新方法及其在跟踪系统中的应用

对于带未知噪声统计且含未知模型参数的单输出系统,本文用现代时间序列分析方法提出了一种新的自校正滤波方法,给出了具有渐近最优性的自校正滤波器,新方法的特点是基于ARMA新息模型通过计算自校正输出预报器和自校正观测噪声滤波器就可得到自校正状态滤波器,文中给出了在跟踪系统中的应用例子,仿真结果说明了新方法的有效性。     

Development of Box–Jenkins type time series models by combining conventional and orthonormal basis filter approaches

A unified scheme for developing Box–Jenkins (BJ) type models from input–output plant data by combining orthonormal basis filter (OBF) model and conventional time series models, and the procedure for the corresponding multi-step-ahead prediction are presented. The models have a deterministic part that has an OBF structure and an explicit stochastic part which has either an AR or an ARMA structure. The proposed models combine all the advantages of an OBF model over conventional linear models together with an explicit noise model. The parameters of the OBF–AR model are easily estimated by linear least square method. The OBF–ARMA model structure leads to a pseudo-linear regression where the parameters can be easily estimated using either a two-step linear least square method or an extended least square method. Models for MIMO systems are easily developed using multiple MISO models. The advantages of the proposed models over BJ models are: parameters can be easily and accurately determined without involving nonlinear optimization; a prior knowledge of time delays is not required; and the identification and prediction schemes can be easily extended to MIMO systems. The proposed methods are illustrated with two SISO simulation case studies and one MIMO, real plant pilot-scale distillation column.     

Time-varying parametric system multiresolution identification by wavelets

In this paper, the problem of time-varying parametric system identification by wavelets is discussed. Employing wavelet operator matrix representation, we propose a new multiresolution least squares (MLS) algorithm for time-varying AR (ARX) system identification and a multiresolution least mean squares (MLMS) algorithm for the refinement of parameter estimation. These techniques can achieve the optimal tradeoff between the over-fitted solution and the poorly represented identification. The main features of time-varying model parameters are extracted in a multiresolution way, which can be used to represent the smooth trends as well as track the rapidly changing components of time-varying parameters simultaneously and adaptively. Further, a noisy time-varying AR (ARX) model can also be identified by combining the total least squares algorithm with the MLS algorithm. Based on the proposed AR (ARX) model parameter estimation algorithm, a novel identification scheme for time-varying ARMA (ARMAX) system is presented. A higher-order time-varying AR (ARX) model is used to approximate the time-varying ARMA (ARMAX) system and thus obtain an initial parameter estimation. Then an iterative algorithm is applied to obtain the consistent and efficient estimates of the ARMA (ARMAX) system parameters. This ARMA (ARMAX) identification algorithm requires linear operations only and thus greatly saves the computational load. In order to determine the time-varying model order, some modified AIC and MDL criterions are developed based on the proposed wavelet identification schemes. Simulation results verify that our methods can track the rapidly changing of time-varying system parameters and attain the best balance between parsimonious modelling and accurate identification.     

On the identifiability of non-Gaussian ARMA models using cumulants

A fixed set of output cumulants of order greater than two guarantees unique identification of known-order causal ARMA (autoregressive moving-average) models, which are driven by unobservable non-Gaussian i.i.d. noise. The models are allowed to be non-minimum-phase, and their outputs may be corrupted by additive colored Gaussian noise of unknown covariance. The ARMA parameters can be estimated either by means of linear equations and closed-form expressions or by minimizing quadratic cumulant matching criteria. The latter approach requires computation of cumulants in terms of the ARMA parameters, which is carried out in the state space using Kronecker products     

Modeling of the grounding resistance variation using ARMA models

This study addresses the problem of modeling the variation of the grounding resistance during the year. An AutoRegressive Moving Average (ARMA) model is fitted (off-line) on the provided actual data using the Corrected Akaike Information Criterion (AICC). The developed model is shown to fit the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on line/adaptive modeling is required. In both cases, and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise is necessary. In this paper, a new method based on the multi-model partitioning theory which is also applicable to on line/adaptive operation, is used for the solution of the above mentioned problem. The simulations show that the proposed method succeeds in selecting the correct ARMA model order and estimates the parameters accurately in very few steps and even with a small sample size. For validation purposes the method introduced is compared with three other established order selection criteria presenting very good results. The proposed method can be extremely useful in the studies of electrical engineer designers, since the variation of the grounding resistance during the year affects significantly power systems performance and must be definitely considered.