- 更多网络例句与自协方差相关的网络例句 [注：此内容来源于网络，仅供参考]
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This section simply introduces the basic principles of the conventional analysis of time series (mean, variance, standard deviation; covariance and correlation coefficient; autocovariance and autocorrelation coefficient), frequency spectrum analysis and wavelet analysis.
本文简单地介绍了传统时序分析（包括平均值、方差和标准差；协方差和相关因子；自协方差和自相关因子）、频谱分析和小波分析的基本原理。
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Stationary and nonstationary processes, including ARIMA processes. Estimation of process mean and autocovariance function. Fitting ARIMA models to data. Statistical tests for white noise. Forecasting. State space models and the Kalman filter. Robust time series analysis. Regression analysis with correlated errors. Statistical properties of long memory processes.
静态和非静态过程(包括 ARIMA 过程)，均值和时间序列分析：自协方差估计，用 ARIMA 模型拟和数据，白噪声序列的统计检验，预报，状态空间模型和 Kalman 过滤，时间序列分析的稳健性，残差具有相关性的回归分析。
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In this paper we use the nonparametric MLE of the marginal distributions of Xt and Yt to construct estimates of the mean, autocovariance and autocorrelation functions of the original signal process {Xt}.
本文在左截断数据模型下估计平稳信号过程的{Xt}均值，自协方差函数，和自相关系数。
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Chapter three (the methods of generating chaotic signal and its applications): In this chapter firstly the mechanism that a simple kinetic system, subsection linear map system, can generate chaotic phenomena is affirmed, the characteristics of correlation function of the chaotic signal generated by this system are simulated, on this basis a simple applicable method for generating chaotic signal is given; secondly some typical circuits generating chaotic signal are designed, also use operational amplifier to design a third -order autonomous circuit with chaotic dynamics. The basic mechanism and typical structures of chaos in the application of communication are introduced systematically and a simple method is provided for generating pseudo random code signal; At last Chaotic signal is applied into the analysis of system characteristic.
第三章(混沌信号产生方法及其应用)首先针对一类简单动力学系统——分段线性映射系统能够产生混沌现象的机理及由该系统所产生的混沌信号的自协方差函数特点进行了证明和计算机仿真，提出了软件产生混沌信号的一种实用方法；其次分析讨论了几种能够产生混沌信号的典型混沌电路，提出了用运算放大器与阻容元件实现三阶自治混沌电路的基本原理；概述了混沌在通信领域中应用的基本原理和典型结构，提出了产生伪随机码信号的一种简单方法；最后将混沌信号用于系统的特性分析中。
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The paper studies the auto-covariance estimation of Gamma distribution by using the asymptotic behavior of student U -statistic,gives the large sample interval estimation for the scale parameter,and carries out the computing simulation according to the method of random simulation.
利用学生氏U－统计量的渐近性质，研究伽玛分布的自协方差估计，同时给出了尺度参数的大样本区间估计，并采用随机模拟的方法进行计算模拟
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The algorithm transforms the complex covariance matrix into a real matrix using an unitary transformation and then the real matrix is inversed and eigen-decomposed for adaptive beamforming so it has a low computation complexity in comparison with the ESB adaptive beamforming algorithm.
该算法利用酉变换将复协方差矩阵转换为实矩阵，然后对其求逆和特征分解进行自适应波束形成，因此其运算量比ESB自适应波束形成算法小得多。
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In the amino-acid composition with auto-covariance function-added approach, the overall resubstitution accuracy is 96.71%, the overall accuracy of Jackknife test is 82.19% and the overall accuracy of the cross-validation test is 86.88% when Wold's index is used.
在氨基酸组成和自协方差函数相结合的方法中，采用Wold等的疏水值时，训练库的自检验的总精度为96.71 %，其Jackknife检验的总精度为82.19 %，检验库的他检验的总精度为86.88 %。
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The performance of a disturbance covariance matrix is degradated in space time adaptive processing when it is estimated with secondary data contaminated by targetlike signals.
针对空时自适应处理中样本协方差矩阵受干扰目标污染时检测性能下降的问题，提出了一种基于知识的空时自适应处理（knowledge aided space time adaptive processing， KASTAP）方法。
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Based on Current Statistical Model for target tracking, a modified maneuvering target model and adaptive filtering algorithm is presented by acceleration and the relation between maneuve trix of process noise.
在&当前&统计模型的基础上，通过修正目标加速度的概率分布、机动加速度与方差的自适应关系及过程噪声协方差矩阵，提出了一种修正的机动目标模型及其自适应跟踪算法。
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By the modem time series analysis method, based on the ARMA innovation model, under the linear minimum variance optimal information fusion criterion, three distributed fusion steady-state optimal Kalman filters, predictors and smoothers weighted by matrices, scalars, and diagonal matrices are presented for multisensor systems with correlated input and observation noises, and with correlated observation noises. The Lyapunov equations and formulas of computing local filtering, predicting and smoothing error variances and covariances are given, which are applied to compute optimal weights. The corresponding three distributed fusion Wiener state estimators are also presented.
应用现代时间序列分析方法，基于自回归滑动平均新息模型，在线性最小方差最优信息融合准则下，对于带相关输入噪声和观测噪声和带相关的观测噪声的多传感器系统，提出了按矩阵加权、按标量加权和按对角阵加权的三种分布式融合稳态Kalman滤波器、预报器和平滑器，其中提出了局部滤波、预报和平滑估值误差方差阵和协方差阵的Lyapunov方程和计算公式，它们被用于计算最优加权，也提出了相应的三种分布式融合Wiener状念估值器。
- 更多网络解释与自协方差相关的网络解释 [注：此内容来源于网络，仅供参考]
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autocorrelogram computer：自相关式计算器
自生相关函数 auto-correlation function | 自相关式计算器 autocorrelogram computer | 自协方差,自协变 autocovariance
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autocovariance function：自协方差函数
autocovariance 自协方差 | autocovariance function 自协方差函数 | autodistributivity 自分配性
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autocovariance：自协方差
Autocorrelator 自动开关器 | autocovariance 自协方差 | autodoping effect 自掺杂效应
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autocovariance：自协方差 自己共分散
■ autocorrelation 自相关 自己相関 | ■ autocovariance 自协方差 自己共分散 | ■ Automated cartography 自动化制图 自動製図器
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autocovariance spectrum：自协方差谱
"autocovariance","自协方差" | "autocovariance spectrum","自协方差谱" | "autoregressive integrated moving average (ARIMA)","自回归积分移动平均"
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autodistributivity：自分配性
autocovariance function 自协方差函数 | autodistributivity 自分配性 | automata 自动机
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automata：自动化理论
自协方差函数 auto-covariance function | 自动化理论 automata | 自动检查 automatic check