Recursive maximum likelihood is used to estimate the parameters.

并用递推的极大似然法辨识参数。

Solving the model by using a maximum likelihood estimation method;

利用极大似然估计进行模型求解；

An approach to combine the maximum likelihood method with the genetic algorithm is proposed.

提出了极大似然法和遗传算法结合的一种方法.

And then the model parameters are estimated by means of MLE ( maximum likelihood estimation ).

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).

其次运用极大似然估计方法对模型的参数进行标定。

An maximum likelihood registration algorithm based on earth-centered earth-fixed (ECEF) coordinate system is presented.

提出了一种基于地心坐标系（ECEF）的传感器极大似然配准算法。

An exact maximum likelihood ( EML ) registration algorithm incorporating the effects of measurement noise is presented.

基于极大似然估计的误差配准 ( EML ) 算法考虑了随机量测误差的影响,性能略优于传统的误差配准算法.

An maximum likelihood registration algorithm based on earth - centered earth - fixed ( ECEF ) coordinate system is presented.

提出了一种基于地心坐标系 ( ECEF ) 的传感器极大似然配准算法.

The result showed that the maximum likelihood algorithm possesses higher stability and suitability on the random measured noise.

结果表明极大似然算法对随机测量噪声具有更高的稳定性和适应性。

The tendency check, goodness of fit check, maximum likelihood estimates(MLE)of the parameters and MTBF for AMSAA model are presented.

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。