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。     

非平稳ARMA信号自校正去卷滤波器

本文用现代时间序列分析方法[1],对于通过已知线性系统被观测的未知非平稳ARMA输 入信号,提出了一种新的自校正递推去卷滤波器,它可用ARMA新息滤波器形式表示,适用 于非最小相位和不稳定的线性观测系统.仿真例子说明了其有效性.     

带有色观测噪声和输入估计的自校正Kalman滤波器及其应用

对于带来知有色观测噪声和未知常的输入的离散线性系统,本文用现代时间序列分析方法,基于ARMA新息模型,提出了一种新的带输入估计的自校正Kalmn滤波器,作为一个应用例子,提出了新颖的带有色观测噪声和输入估计的自校正α—β跟踪滤波器,仿真结果说明了其有效性。     

非平稳ARMA信号自校正滤波器及其应用

本文处理带白色观测噪声的非平稳ARMA信号估计问题.应用状态空间方法和现代时 间序列分析方法[1],基于ARMA新息模型,提出了非平稳ARMA信号自校正滤波器,推广了 Hagander和Wittenmafk的结果[2],并给出了在雷达跟踪系统和检测数据数字滤波方面的应 用.仿真结果说明了本文结果的实用性和有效性.     

稳态Kalman滤波器增益新算法

应用现代时间序列分析方法,基于ARMA新息模型,提出了稳态Kalman滤波器增 益的两种简单的新算法,并证明了它们的等价性.应用ARMA新息模型参数的递推辨识器 伴随新算法,可实现自校正Kalman滤波器.仿真例子说明了其有效性.     

自校正α-β跟踪滤波器

本文用现代时间序列分析方法对雷达跟踪系统提出了一种新的自校正α-β跟踪滤波器, 它有如下优点:1)可处理带未知噪声统计和含未知模型参数的跟踪系统;2)基于ARMA新 息模型的在线辨识,可简单地计算α-β滤波器的参数;3)避免解稳态Riccati方程;4)具有渐 近最优(自校正)性.仿真例子说明了其有效性.     

自校正滤波器在卫星定位中的应用

设计了一种新的基于ARMA模型的自校正卡尔曼滤波器,对卫星定位误差模型参数进行分段在线估计,根据误差模型估计参数直接求取滤波增益阵.并提出了一种直接计算滤波误差方差阵的方法,对两个不同的定位系统进行信息融合.仿真结果表明,在未知噪声统计特性的情况下,自校正卡尔曼滤波器能有效过滤观测噪声,基于它的信息融合规则能够有效提高定位精度.计算过程简单,并可以在线建模.     

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。     

带有色观测噪声的多变量自校正滤波器和平滑器

应用时间序列分析方法和射影理论,基于观测过程的ARMAX新息模型,对于带有色观测噪声的多变量系统,本文提出了两类自校正滤波器和平滑器,它们分别可应用于语音信号辨识和动态船舶定位。     

自校正α—β—γ跟踪滤波器

对于含有未知模型参数和带未知噪声统计的一类跟踪系统,基于ARMA新息模型的在线辨识,本文提出了一种新颖的自校正α-β-γ跟踪滤波器,仿真结果说明了它的有效性。     

Self-convergence of weighted least-squares with applications tostochastic adaptive control

A recursive least-squares algorithm with slowly decreasing weights for linear stochastic systems is found to have self-convergence property, i.e., it converges to a certain random vector almost surely irrespective of the control law design. Such algorithms enjoy almost the same nice asymptotic properties as the standard least-squares. This universal convergence result combined with a method of random regularization then easily can be applied to construct a self-convergent and uniformly controllable estimated model and thus may enable us to form a general framework for adaptive control of possibly nonminimum phase autoregressive-moving average with exogenous input (ARMAX) systems. As an application, we give a simple solution to the well-known stochastic adaptive pole-placement and linear-quadratic-Gaussian (LQG) control problems in the paper     

Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique

For multivariable equation-error systems with an autoregressive moving average noise, this paper applies the decomposition technique to transform a multivariable model into several identification sub-models based on the number of the system outputs, and derives a data filtering and maximum likelihood-based recursive least-squares algorithm to reduce the computation complexity and improve the parameter estimation accuracy. A multivariable recursive generalised extended least-squares method and a filtering-based recursive extended least-squares method are presented to show the effectiveness of the proposed algorithm. The simulation results indicate that the proposed method is effective and can produce more accurate parameter estimates than the compared methods.     

Structure of stationary finite observation records of discrete-time stochastic linear systems

We discuss the structural features of finite observation records from discrete-time stationary Gaussian stochastic processes with rational power spectra. The processes may be viewed as arising from discrete-time linear systems excited by white noise or as autoregressive-moving average processes. The latter parametrization is chosen for convenience, and the existence of nontrivial sufficient statistics is studied. It is shown that only autoregressive processes have sufficient statistics whose dimension is less that the number of observations. Some connections with the theory of stochastic realization are described.     

Computing and estimating information matrices of weak ARMA models

Numerous time series admit weak autoregressive-moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent nor martingale differences. The statistical inference of this general class of models requires the estimation of generalized Fisher information matrices. Analytic expressions are given for these information matrices, and consistent estimators, at any point of the parameter space, are proposed. The theoretical results are illustrated by means of Monte Carlo experiments and by analyzing the dynamics of daily returns and squared daily returns of financial series.     

On estimating the order of an ARMA process

An order estimation scheme is proposed for scalar autoregressive-moving average processes. It is based on the determination of the rank of estimated matrices. Numerous heuristic tests have already been derived around the relations existing between the rank of covariance matrices and ARMA orders. Using results from matrix perturbation theory we indicate how to build and justify statistical tests which permit decisions about e.g. how many eigenvalues should be declared equal to zero. The usefulness of these asymptotic developments is verified by simulations. The results are in agreement with the predictions for quite small sample sizes and the performances are satisfactory.     

Hierarchical gradient based iterative parameter estimation algorithm for multivariable output error moving average systems

According to the hierarchical identification principle, a hierarchical gradient based iterative estimation algorithm is derived for multivariable output error moving average systems (i.e., multivariable OEMA-like models) which is different from multivariable CARMA-like models. As there exist unmeasurable noise-free outputs and unknown noise terms in the information vector/matrix of the corresponding identification model, this paper is, by means of the auxiliary model identification idea, to replace the unmeasurable variables in the information vector/matrix with the estimated residuals and the outputs of the auxiliary model. A numerical example is provided.     

多变量系统的模糊神经网络控制模型及其应用

本文综合模糊控制系统与人工神经网络的优点，提出了一种多变量系统的模糊神经网络控制模型并给出了其建模方法，该方法适合于多变量系统的建模及其模糊控制器的设计。笔者以此方法建立了渣贫化电炉生产过程的模糊神经网络控制模型并开发出相应的决策支持系统，该系统自１９９２年６月投入生产现场使用以来，一直稳定可靠地正常运行，取得了令人满意的效果和显著的经济和社会效益。     

对角CARIMA模型多变量广义预测控制改进算法

为了增强多变量广义预测控制算法(MGPC)的实用性,对其实现形式进行了进一步的简化.利用对角CARIMA模型的结构特点,先对系统中单个输出变量期望值的自由响应部分进行分解推导,将其表达成自由响应项系数与系统输入输出变量已知值乘积的形式,得到此输出变量的预测表达式,然后将系统所有输出变量的预测表达式代入目标函数中,得到的控制增量等于控制器系数与参考轨迹、过程输入输出历史数据的乘积.控制器系数只与模型参数及设计参数有关,求解控制量时不再需要进行模型输出预报,控制器结构简单,实现容易.对比实验结果表明了该方法保持了常规MGPC方法的优秀控制性能.     

多变量系统控制器的参数满意优化设计

为了将满意优化拓展到多变量系统中以解决多变量控制系统线性二次型(LQ)控制器设计中加权系数阵确定难的问题,提出多变量系统满意优化设计方法,通过设计满意度函数,构造出多变量系统的满意优化数学模型,并用改进遗传算法实现二级倒立摆系统LQ控制器满意优化设计.仿真结果显示,系统具有更满意的综合性能指标.证实了该方法的有效性和实用性.本文的研究对多变量系统优化设计具有一定的参考价值.