其次运用极大似然估计方法对模型的参数进行标定。
And then the model parameters are estimated by means of MLE (maximum likelihood estimation).
给出了趋势检验、AMSAA模型的拟合优度检验及模型参数的极大似然估计方法。
The tendency check, goodness of fit check, maximum likelihood estimates (MLE) of the parameters and MTBF for AMSAA model are presented.
结论基于MCECM算法的极大似然估计方法可用于估计非线性因子分析模型的参数。
Conclusion the maximum likelihood method based on MCECM algorithm can be used to estimate the parameters of non-linear factor analysis model.
通过应用极大似然估计方法，解决了产品寿命和维修时间分布中未知参数估计的问题。
By using the maximum likelihood estimation method, the unknown parameters in the lifetime and repair time distribution are solved.
研究了非线性连续—离散系统极大似然估计方法的实现及其在飞行器气动参数辨识中的应用问题。
The implementation of maximum likelihood estimation method to the nonlinear continuous ?discrete systems and application to aerodynamic parameter identification for vehicle are studied.
针对传统电器可靠性失效分析方法中存在的问题，提出了一种电器可靠性失效分析极大似然估计方法。
To be direct against problems of traditional apparatus reliability failure analysis method, this paper presents a failure analysis method based on maximum likelihood estimation method.
该方法依据极大似然原理将来自不同母体(均值相同、方差不同)的随机样本有效融合，得到新的母体均值估计量。
According to maximum likelihood theory, it fuses random samples coming from different matrix (same mean different variance) in an effective way, and gains a nwe estimator of matrix mean.
频域极大似然法则是一种有效的方法，它具有频域计算简单，同时估计又是渐进无偏和一致的优点。
The frequency domain maximum likelihood method is a very efficient method, the advantages of which are simple in computing, un biased and consistent.
方法借助于更新过程的理论，给出了前一种寿命试验的极大似然估计并举出模拟例子。
Methods Based on the theory of renewal process the maximum likelihood estimators of parameters of the former are given.
为解决这一问题，本文将极大似然法引入其概率图，提出了一种近似的参数估计方法并给出了算例。
To solve this problem, this paper provides a approximate parameter estimating method by using of the Maximum Likelihood method on the probability paper, and a calculation example is offered.
然后就动态系统未知时延参数的估计问题提出了重构系统输入的估计方法，并以极大似然估计迭代算法为基础给出了一套估计动态系统时延参数的算法。
And then a method called system input reconstruction for estimating dynamic system's time lags and an algorithm based on maximum likelihood estimation for extracting the time lags are presented.
本文提出了一种有效的飞行仪器偏差估计的极大似然方法。
An efficient maximum likelihood method for the estimation of instrumentation errors for flight is presented.
研究了广义预测控制的模型反馈校正方法，将递推极大似然法和遗忘因子递推最小二乘法结合起来，给出了一种改进的递推极大似然参数估计算法。
Model feedback correction algorithm of GPC has Benn studied. Combining the RML and forgetting factor RLS, an improved RML parameter estimation method has been given.
因此在JM模型的基础上，提出了排错时间为负指数分布的软件可靠性模型及本模型的极大似然参数估计方法。
Based on JM model, we propose a software reliability prediction model involving fault-remove time which followed exponential distribution.
进行了新方法、原贝叶斯高分辨方位估计方法与多重信号特征法（MUSIC）和极大似然估计法（MLE）的性能比较研究，揭示了新方法的优越性。
The new method and original Bayesian high-resolution DOA estimator are compared with other typical methods like MUSIC and MLE, and the superiority of the new method is revealed.
应用线性回归技术和极大似然法原理，给出了概率曲线及其置信限的估计方法。
A method for estimating the curves and their confidence bounds is developed by a linear regression technique and a maximum likelihood principle.
处理生存分析观测数据使用的参数估计方法有很多，极大似然估计法是最常见的一种估计方法。
There are many methods to deal with measuring data in survival analysis. Maximum likelihood estimation is the most popular one.
本文对带有右截尾数据的有重复因子试验，提出了另一种分析位置效应和散度效应的方法：首先，在每一个试验点，对重复试验观察值用极大似然法估计出均值和方差；
A method is presented for estimating the location and dispersion effects from these experiments. Firstly, we estimate the variance and the mean of each cell with maximum likelihood;
与极限平衡法和极大似然法的估计结果进行了比较，可以看出，神经网络方法具有推广预测精度高、自学习功能强、考虑不确定性能力强等特点。
The learned knowledge is then used for extrapolating prediction of the safety factor of new slope, Compared with the safety factors predicted by the limit equilibrium and m…
因此在JM模型的基础上，提出了排错时间为负指数分布的软件可靠性模型及本模型的极大似然参数估计方法。
A software reliability model for substation automation system based on improved JM model is set up in this paper.
与极限平衡法和极大似然法的估计结果进行了比较，可以看出，神经网络方法具有推广预测精度高、自学习功能强、考虑不确定性能力强等特点。
The learned knowledge is then used for extrapolating prediction of the safety factor of new slope, Compared with the safety factors predicted by the limit equilibrium and maximum likelihood …
与极限平衡法和极大似然法的估计结果进行了比较，可以看出，神经网络方法具有推广预测精度高、自学习功能强、考虑不确定性能力强等特点。
The learned knowledge is then used for extrapolating prediction of the safety factor of new slope, Compared with the safety factors predicted by the limit equilibrium and maximum likelihood …
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