渐近的

The author discusses the MLE method of the evaluation model and its convergence and asymptotic property.

3重点讨论了评估模型参数的极大似然估计方法，以及MLE的收敛性和渐近性。

The asymptotic global convergence of the proposed algorithm is analyzed by using the Lyapunov function approach.

通过Lyapunov函数方法分析了算法的渐近稳定性能，模拟结果验证了算法的有效性。

The influence of the vibration modes unmodeled on the conditions for the asymptotic convergence is discussed.

首次得出了学习系数与渐近收敛性的关系，并运用数学工具做了严格的证明。

Some small sam-ple and large sample superiority of this estimate such as the unbiasedness,convergence,asymptotic normality and the property of simulation,are obtained.

我们得到分块拟回归的小样本和大样本性质，如无偏性、均方收敛性、强收敛性和渐近正态性，并讨论了曲线拟合的性质。

Second, when the system does not satisfy such detectability but satisfy other conditions, there is a gain matrix ensuring the asymptotic convergence of the observer error, and the sufficient conditions and the method of solving gain matrix are obtained similarly.

其次， 当系统不满足该可检测性，但满足其它条件时，仍存在使得观测误差渐近收敛的增益矩阵，并类似地得到了增益矩阵满足的充分性条件和计算方法。

A differential difference type adaptive law and an adaptive iterative learning controller are constructed to ensure the asymptotic convergence of tracking errors in the sense of square error norm on the finite interval, by introducing a Lyapunov-like function, a sufficient condition of the convergence of the method is given.

通过引入微分－差分自适应学习律，设计了一种自适应控制策略，使得跟踪误差在一个有限区间上的积分渐近收敛于零；通过构造Lyapunov-like函数，给出了闭环系统收敛的一个充分条件。

Firstly, the multi-user detector that relatively integrity in theory and proximal to optimal multi-user detector is discussed and Decor elating Detector, Minimum Mean Square Error Detector, Optimal linear Detector together with the multi-capability indexes output energy, SNR, bit error rate, asymptotic efficiency, convergence etc.

本文首先对理论上及性能上接近最佳多用户检测的次最佳多用户检测进行理论研究，计算并分析了解相关检测器、最小均方误差检测器、最佳线性检测器的性能指标(输出能量、信噪比、渐近有效性、误码率以及收敛特性等)。

This dissertation is concerned with some problems regarding the asymptotic property of nearest-neighbor density estimator for α—mixing and nagetive quadrant dependent samples. The results extend the corresponding theorems in i.i.d.

本文主要考虑了最近邻密度估计在α—混合序列和两两NQD混合序列情形下的大样本性质，推广了独立同分布和其它相依情形下最近邻密度估计的大样本性质，如相合性、渐近正态性等。

We show that our estimator achieves the same asymptotic efficiency as Lin and Carroll's estimator, but performs better in finite samples.

估计量达到了与Lin和Carroll的估计量相同的渐近有效性，且在有限样本情形下表现更好。

Further more,we compute the asymptotic expansion of sums involving generalized harmonic number by singularity analysis method.

进一步地，利用奇异性分析方法，求出涉及广义调和数的一些和式的渐近展开。

We are in a real jam.

我们的麻烦大了。

Hey, it's Ahmet from India, that foreign exchange guy.

看，那是印度的阿曼特，国际交换生