Conclusion Optimal recursive estimation for predicting and filtering of state for this singular system are obtained.
结论得到了该系统状态的最优预测和滤波递推方程。
This paper deals with the closed form recursive estimation for parameters of almost periodic moving average (APMA) signal using higher order cyclic statistics.
本文利用高阶循环统计量讨论几乎周期滑动平均(apma)信号参数的闭式递推估计。
By using this material in Guangxi, the application of the linear autoregression model associated with weighting and recursive estimation was introduced for forestry.
以广西实际资料具体阐述了线性自回归加权递推模型在大林业中的应用。
By using recursive estimation in cointegrated VAR model, we analyze the relationship between real estate financial structure and growth of real estate economy in China.
本文运用VAR模型协整关系的递归估计方法，对我国房地产金融结构和房地产经济增长的关联性进行了分析。
The principle of Recursive Bayesian estimation was introduced which was the basis of Particle filter, and the significance of importance function to the design of particle filter was illustrated.
介绍了作为粒子滤波理论基础的递推贝叶斯估计的基本概念，说明了重要性函数对于粒子滤波器的设计是至关重要的。
The direct form for the recursive least squares estimation via matrix QR decomposition with one step data updated is given .
给出了当数据一步更新时，利用矩阵QR分解进行最小二乘估计的直接递推形式。
A new adaptive recursive algorithm for eigenvector estimation based on the relations between KLT and sliding DCT was presented.
基于KLT与滑动dct的相似关系，提出了一种新的特征向量自适应递推估计算法。
In the sense of linear unbiased minimum variance estimation, a global optimal recursive state estimation algorithm for this discretized linear system is proposed.
在线性无偏最小方差估计准则下，推导出了该离散化后所得系统的全局最优递推状态估计算法。
According to the structure characteristics of a time varying time series model, a new recursive parameter estimation algorithm of the time varying ar model is proposed.
根据对一类时变时间序列模型结构特点的研究，提出了一种时变ar模型的递推参数估计算法。
Finally, a synthesis method for recursive parameter estimation of a stochastic system is also discussed.
最后讨论了随机系统参数递推估算的综合方法。
Recursive Measurement Error estimation Identification (RMEEI) method for dad data analysis in power system state estimation is proposed in this paper.
本文提出不良数据检测和辨识新算法——递归量测误差估计辨识法(RMEEI法)。
When the noise variance is known, and it's process is stationary, the signal can be enhanced by use of the recursive least mean-square estimation.
当噪声的方差已知，且过程是平稳的，应用递推最小方差估计，能够增强信号。
It can overcome the effect of bad data on parameter estimation, and has strong robustness, and can track time-varying parameters because the discontinuous recursive computation was applied.
该方法由于采用了间断迭代，因此可以克服坏数据对参数估计的影响，且具有较强的鲁棒性，又可以跟踪时变参数。
To reduce this error, a recursive channel estimation algorithm was proposed to compensate the channel estimation of data field to increase the precision of the channel estimation of single timeslot.
为了降低这种误差，提出了循环迭代的算法，通过对数据域信道估计进行补偿，提高了单个时隙信道估计精度。
Especially the latest fast estimation method, then expands it on this base, ratiocinates the recursive algorithm based on extreme order statistics, realizing the dynamic estimation of parameters.
特别是最新的快速估计方法，并在其基础上进行了扩展，推导出基于极端顺序统计量的递推算法，实现了参数的动态估计。
By introducing the so-called blending partial differences, a recursive algorithm is given as well as a numerical example. The characteristic theorem and an error estimation are also presented.
通过引入所谓的混合偏差商，给出一个递推算法及一个数值例子，进一步给出了其特征定理和误差估计。
Many recursive identification algorithms used for on-line estimation of parametersin dynamic systems can also be applied to data fitting for off-line problems.
许多用于动态系统在线参数估计的递推辨识算法，同样可以用于离线数据拟合问题。
In this paper, a method of process quality diagnosis using hypothesis testing for residual sequence of ARMA innovation model estimation error by recursive maximum likelihood method was studied.
本文基于辨识 ARMA新息模型生成估计残差序列 ，再对残差序列的平均值和无偏方差进行假设检验 ，可实现工序质量的异常诊断。
After inspecting the properties of the covariance matrix, a recursive method of system parameter estimation is derived upon LSE.
在研究协方差矩阵特性的基础上，给出一种最小二乘辨识系统的递阶算法。
A variable-order recursive extended least squares method and a new criterion of order estimation is derived in the paper .
本文给出变阶式递推增广的最小二乘法及阶估计准则。
Compared with the recursive extended least squares algorithms, the proposed two algorithms have fast convergence rates and can produce highly accurate parameter estimation.
与常规递推增广最小二乘算法相比，提出的方法具有更快的收敛速度，能产生更高精度的参数估计。
A cost function is presented, and by applying Gaussian-Newton type recursive prediction error based method, a stable and efficient online frequency estimation algorithm is derived.
在此基础上，建立了最小方差损失函数，并结合高斯·牛顿预测误差方法，提出了稳定的，高性能的，在线的复频率直接估计算法。
A cost function is presented, and by applying Gaussian-Newton type recursive prediction error based method, a stable and efficient online frequency estimation algorithm is derived.
在此基础上，建立了最小方差损失函数，并结合高斯·牛顿预测误差方法，提出了稳定的，高性能的，在线的复频率直接估计算法。
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