确定性信号的收敛速率慢于不相关信号的速率。
The convergence rate of deterministic signal is slower than the one of uncorrelated signal.
一些温和的条件下证明了全局收敛性和线性收敛速率。
The global convergence and linear convergence rate are proved under some mild conditions.
在一合理的假设下证明了该算法具有Q-2阶收敛速率。
The Q-2 order convergence is proved under a reasonable assumption.
我们对这类算法的收敛速率做了估计，最后还给出了其单调收敛定理。
We estimate the convergence rate of this method and give its monotone convergence theorems.
同时，采用时变交叉概率因子的方法以提高算法的全局搜索能力和收敛速率。
At the same time, the method of time-varying crossover probability factor was adopted to improve the global searching ability and convergence speed of MDE.
但盲估计算法，如子空间分解法等，需要较大的样本值，收敛速率慢，不利于实时信道估计。
But the blind estimation algorithm, for example, the sub-space decomposition, is not good for real time estimation because it requires large received signals, and has low estimation convergence rate.
并发现对所提出的EB检验，在某些条件下，具有渐近最优性的收敛速率，能够任意接近于1。
It is discovered that convergence rate with asymptotical optimality for the proposed EB test can arbitrarily approach to 1 under certain conditions.
实验结果表明，该算法收敛速率快，寻优能力强，为指挥员提供了实时有效若干可行的分配方案。
The simulated experiments prove that the algorithm comes to excellent optimal solution with high convergence rate.
提出一种基于正交小波变换的自适应语音消噪改进方法，这种方法可以提高自适应语音消噪过程的收敛速率。
An improved method based on orthogonal wavelet transform for adaptive speech denoising is proposed, which can increase the convergence rate of the adaptive speech denoising process.
然而，在实际应用中，随着子带数目增加而增加的收敛速率却最终受限于不理想的滤波器组和有限字长效应。
However, in practice, the convergence improvement gained by increasing the number of subbands is ultimately limited by nonideal filter Banks and finite-word-length effects.
本文提出了一种基于正交小波变换的自适应语音消噪改进方法，这种方法可以提高自适应语音消噪过程的收敛速率。
A method of adaptive speech denoising based on orthogonal wavelet transform is proposed in this paper. This method can increase the convergence rate of the adaptive processing.
他们也在问，是否就是因为存在管制而减缓了落后国生产率向先进国收敛的速率。
They also ask whether regulation slows the rate at which laggard countries close the gap between themselves and the leaders.
结果表明，网络的增益、学习速率和动量是影响网络收敛和稳定性的关键参数。
Restults show that the gain, learning rate and momentum are critical for network convergence and stability.
该模型有自组织和自学习的功能，可以根据每次学习误差的不同，不断调整学习速率，加速收敛过程，充分排除数据样本的随机性影响。
The network model can organize and study itself, according to different study error, continuously adjust the study rate, and accelerate refrain process, expel influence of the data sample.
在神经网络自学习过程中，引入了自适应学习速率和动量法，加快了网络的收敛速度，提高了网络的辨识精度。
During the self learning process, the adaptive learning rate and momentum gene are introduced to accelerate the rate of convergence and advance the identify accuracy.
结果表明基于分解协调的人工鱼群算法收敛性好，提高了计算速率，较好的解决了作物优化配水大系统中常见的变量维数高、约束方程多等问题；
In the result, the artificial fish school algorithm based on decomposition and coordination method shows its advantages on computing speed, convergence and solving dimension difficulty.
桩基础具有稳定性好、沉降量相对小且均匀、沉降速率小且收敛快、承载力高等优点。
Pile foundation has many advantages, such as good stability, relatively small and uniform subsidence, small sedimentation rate and fast convergence, and high bearing capacity.
本文讨论了非线性多组分液相色谱速率模型求解过程中两种不同的插值方式即线性插值和拉格朗日插值对模型计算结果收敛速度的影响。
Two interpolation methods, which are linear interpolation method and the Lagrange interpolation method, are compared for calculating the rate model of multi-component nonlinear liquid chromatography.
侧重于收敛的速率和整体、局部分析。
We lay particular emphasis on analysis of global and local convergence and rate of convergence.
传统的强化学习模型在整个学习过程中使用恒定学习速率，导致在未知环境下收敛速度慢且适应性差。
The learning process use the constant learning rate in the traditional reinforce learning model, because of that robot learn in a low convergence speed and with the poor adaptation.
针对BP算法收敛速度慢的特点，在隐含层上加入了关联节点，改善了网络的学习速率和适应能力。
Aiming at the slow convergence rate of BP neural network, append a correlative node on hidden layer, improve the adaptive ability and rate of studying of neural network.
使用常规pid控制很难满足手指精确位置控制的要求，而采用依据BPNN原理设计成的常规单神经元pid控制器又因学习速率低，收敛速度慢，控制效果不能令人满意。
But if single neuron PID controller designed in terms of BPNN Theory is adopted, the control effect is not satisfactory because the learning rate and speed of convergence are slow.
详细地讨论了增益、学习速率、动量等网络参数对神经网络收敛速度和导数脉冲伏安法计算结果的影响。
The effects of neural network parameters including gain, learning rate, and momentum on network convergence and DPV computation results have been investigated.
详细地讨论了增益、学习速率、动量等网络参数对神经网络收敛速度和导数脉冲伏安法计算结果的影响。
The effects of neural network parameters including gain, learning rate, and momentum on network convergence and DPV computation results have been investigated.
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