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- A new covariance matrix estimator is developed, and a formula for fast estimation of regularization parameters is derived. 发展了一种新的协方差矩阵估计器,导出了一个快速估计正则化参数的公式。
- There are obvious differences between the variance-covariance matrix estimated by tile two methods and by the usual pre-test covariance matrix. 结果两种方法估计的方差-协方差阵都与常用的先验协方差阵有明显区别;
- Experiments on face recognition and spectrum pattern recognition are conducted to investigate the classification accuracy with the developed covariance matrix estimator. 利用人脸和光谱模式识别的实验,对所发展的协方差矩阵估计器进行了探索。
- An Adaptive Kalman Filter Combining Variance Component Estimation with Covariance Matrix Estimation Based on Moving Window 基于移动开窗法协方差估计和方差分量估计的自适应滤波
- covariance matrix estimation 协方差阵估计
- For the constraint system of covariance matrix being uncertain, a robust moving horizon estimation (MHE) strategy is discussed. 摘要针对噪声方差不确定的约束系统,讨论了一种鲁棒滚动时域估计(MHE)方法。
- Meanwhile, the result is refined using frequency-domain correlation and covariance matrix of estimation error in minimum mean-squared error sense. 对滤波的结果,利用信道的频域相关函数和估计误差的协方差矩阵,在频域进行最小均方误差意义上的优化。
- estimation error of the covariance matrix 协方差矩阵估计误差
- IMITATING COVARIANCE MATRIX OF DISPLACEMENT DATA AND ITS APPLICATION IN CALCULATION OF CRUSTAL STRAIN FIELD AS A WHOLE? 位移观测值协方差矩阵模拟及其在整体解算地壳应变场中的应用?
- When the observation noise is temporal correlation, Kalman Filter will not be able to achieve optimization, and its covariance matrix will be wrong. 在观测噪声不满足时间不相关的假设情况下,卡尔曼滤波将达不到最优滤波效果,并且其误差协方差阵也是错误的。
- This Monte Carlo study, utilizing variable sampling approach, is to systematically investigatethe estimation of covariance matrices from incomplete data. 摘要本研究以电脑模拟资料方式,探讨变数取样各种因素对共变数矩阵估计之影响。
- DFT is used to estimate the covariance matrix of downlink channel of the path, not calculating DOA. 在不求出DOA的情况下,采用离散傅里叶变换来估计该路径下行信道协方差矩阵。
- Fault detection and classification (FDC) via sample covariance matrix is thus very important. 因此,对于共变异矩阵的错误侦测与鑑别(FDC)也越来越重要。
- Realized covariance matrix is a new measurement of the volatility and correlation of portfolio. 摘要“已实现”协方差矩阵是对投资组合波动性及相关性的一种全新的度量方法。
- The algorithm uses the sub-block of covariance matrix which is not affected by noise to estimate the parameters of DOA. 使用该算法和两平行均匀线阵可实现二维来波方向的快速估计。
- By averaging the previous results, a new covariance matrix is obtained.It is a full rank matrix. 对其进行平均后,所得到的相干信号协方差矩阵具有满秩性。
- By analyzing the weighted covariance matrix of a local neighborhood, its least-squares plane was estimated. 对局部邻域进行加权协方差分析,估计出该部域的最小二乘拟合平面。
- The FIVAR model based on realized covariance matrix is good basis of studying co-persistence. 针对“已实现”协方差阵建立的FIVAR模型为进一步研究波动的协同持续性提供了基础。
- A new online coupled algorithm for PCA and MCA which can simultaneously extract the eigenvectors and eigenvalues of the covariance matrix with coupled iterations is obtained. 给出能同时得到主成分分析或小成分分析所要求的特征值和特征向量的实时算法。
- With the characters of covariance and pseudo covariance matrix combined, the cost function feeds more useful information into the algorithm. 由于充分利用了非正则复向量不相关的特性,获得了更多有用的信息量,因此算法的收敛速度更快、提取效果更好,并且在线方式下算法更加稳定。