采用加权的均方误差准则来优化估计模型的参数，实现对测量序列的抗扰最佳估计。
To obtain the best estimation of anti-jamming for measured series, the rule of weighted-square error is introduced to optimize the parameters of estimating model.
通过公式推导，从理论上证明了增加加权数目并不能保证减小稳态均方误差。
It has been proved that the increasement of weights number can not ensure the reduction of steady state square error.
该文详细讨论了LMS算法中输入信号相关性、加权数目和稳态均方误差的关系。
This paper discusses the relationship among the correlation of input signal, weights number and steady state square error of the LMS algorithm in detail.
新算法使用时变遗忘因子对误差进行指数加权平均来估计均方误差，并使用该因子改变自适应迭代过程中滤波器系数向量的更新方向。
In the new algorithm, a time-variant forgetting factor is introduced to estimate the Mean Square Er-ror (MSE) and change the updating direction of adaptive filter coefficient vector.
利用最小均方误差（MMSE）准则求得相关干扰条件下阵列最优加权矢量和输出最小均方误差；
Then, the optimal weighted vector and the MMSE minimum mean square error were obtained using the MMSE principle.
本文指出最小均方算法并不能使均方误差最小，证明了最小均方算法实际上是一种加权最小二乘算法。
It is shown in this paper that the Least-Mean-Square algorithm cannot minimize the mean square error, and that it is actually a kind of weighted Least-Square algorithm.
本文指出最小均方算法并不能使均方误差最小，证明了最小均方算法实际上是一种加权最小二乘算法。
It is shown in this paper that the Least-Mean-Square algorithm cannot minimize the mean square error, and that it is actually a kind of weighted Least-Square algorithm.
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