Finally, the random error model of HRG is established by using time series analysis method.
最后，采用时间序列分析方法建立了半球谐振陀螺的随机误差模型。
According to the homogeneity test for error variance on total-effect random model with three factors in double repeated experiment, the specific formulae of test statistics and test rule are deduced.
研究两次重复试验三因子全效应随机模型误差方差的齐性检验问题，推导了检验统计量和检验规则的具体表达式。
According to this model and the Kalman filter arithmetic, the FOG random error was filtered in real time in the process of initial alignment and navigation of FOG inertial navigation system.
根据该模型，采用卡尔曼滤波算法，实现了光纤陀螺惯导系统在对准与导航过程中光纤陀螺随机误差的实时滤波。
The basic characteristic of the linear model is the unknown parameter of the model is linear and it also includes the linear random error.
线性模型的基本特征是，模型的未知参数是线性的，同时又线性的包含了随机误差项。
Triggering time synchronization error model is derived, and based on phase noise model of frequency source, random synchronization error is considered.
本文建立了触发时间同步误差模型，结合频率源相位噪声模型，初步分析了随机型时间同步误差对成像的影响。
The results show that the filter based on the time sequence model can effectively decrease the random error.
结果表明，基于时间序列模型的卡尔曼滤波器有效地减小了随机误差。
The paper sets up a time sequence model of laser gyro random error and processes the drift data by Kalman filter based on the model.
在对激光陀螺漂移数据建立时间序列模型的基础上，对激光陀螺的漂移数据进行了卡尔曼滤波。
Furthermore, a random drift error model for IFOG is built by the method of time series analysis.
此外，采用时间序列分析方法，建立了IFOG的随机漂移误差模型。
In this paper, the analysis model of structural fuzzy-random reliability of considering human error probability possibility distribution function is derived.
由此给出了人误概率的可能性分布函数，建立了基于人误概率可能性分布的结构模糊随机可靠度分析模型。
The precision of mixed model considering plot random effects, time series error autocorrelation and different plantation density at one time is better than that of ordinary regression analysis method.
同时考虑样地的随机效应、观测数据的时间序列相关性及不同初植密度的混合模型模拟精度比传统的非线性回归方法模拟精度高。
The precision of mixed model considering plot random effects, time series error autocorrelation and different plantation density at one time is better than that of ordinary regression analysis method.
同时考虑样地的随机效应、观测数据的时间序列相关性及不同初植密度的混合模型模拟精度比传统的非线性回归方法模拟精度高。
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