We want to accelerate our learning speed.
我们想加快学习的速度。
Learning speed equals the speed of success!
学习的速度等于成功的速度！
You can double your vocabulary learning speed by learning words in pairs.
在对学习的话，您可以双击你的词汇学习的速度。
If your learning speed is 50, I hope you to increase horsepower, toward the 100.
如果你现在学习上的速度是50码的话，那么老师希望你增加马力，向着100码进军。
We use conjugate gradient method to improve the learning speed of the premise parameters.
用共轭梯度法提高其前提参数的学习速度。
If an organization need survive, their learning speed must be faster or equal to change speed.
一个组织如果想生存，其学习的速度必须大于或等于变化的速度。
The emulation demonstrates that this algorithm can definitely enhance the learning speed and training accuracy.
仿真表明该算法能大大提高学习速度和训练精度。
In addition, switch to normal conversation speed material as soon as you are comfortable with learning speed material.
除此之外，一旦你适应了学习材料的速度，就把其更换为正常交谈语速的材料。
In the international language-learning environment, you can accelerate language learning speed through immersion study.
在海外的语言环境下，沉浸式的学习对于短时间内迅速提升听、说、读、写能力非常有帮助。
Aim To study the standard BP algorithms local minima and learning speed problems and propose the scheme for improvement.
目的对BP学习算法中存在的大量局部极小点以及收敛速度慢问题进行研究并提出相应的改进方案。
BP neural networks with pattern extended input are used to estimate control parameters, and the learning speed is increased.
并采用具有模式增强输入的BP网络进行决策参数估计，加快学习的收敛。
However, its learning speed is slow in learning more advanced copies and a complicated output-input projection relationship.
然而对于学习样本较多、输入输出映射关系复杂的情况，学习速度较慢。
The modified neural network's learning speed is much increased because of the linear network and the recursive least square.
由于线性网络的引入及递推最小二乘法的使用，大大提高了网络的学习速度。
Rapid learning speed, few training steps and high fidelity are advantages of the model. The maximal deviation is no more than 5%.
该模型学习速率快，训练步骤少，逼近程度高，最大偏差不大于5％，为三相循环流化床蒸发器的传热计算提供了可靠的方法和理论基础；
The intrinsic defects of artificial neural network, e. g. , its slow learning speed. existence of partial minimum points, are solved.
有效地克服了人工神经网络学习速度慢、存在局部极小点的固有缺陷。
General regression neural network is proved with certain superiority in the ability of approaching, classification and learning speed.
广义回归神经网络在逼近能力、分类能力和学习速度方面具有较强优势。
Computer simulation using concrete examples shows that the suggested algorithm provides satisfied fitting features and faster learning speed.
计算机数字实例模拟表明，该算法具有学习速度快、拟合效果好等特点。
Our previous work has shown that the network was effective in improving two difficulties, a convergence to local minimal and a slow learning speed.
并对以前神经网络中的两个难点：局部极小和收敛速度慢的问题进行分析。
The paper analyzes the existing algorithms and their strengths, weaknesses and the problems, depending on the learning accuracy and learning speed;
在概述其机理的基础上，从如何提高学习精度与学习速度着手，分析了现有算法及其优缺点和需要改进的问题；
The experimental results show that the network learning speed can be increased and the nonlinear errors of the sensors can be reduced by using BPNN.
实验结果表明采用BP神经网络可以提高网络收敛速度，大大减小传感器线性误差。
To improve learning speed, a novel method for properly initializing the parameters (weights) of training complex-valued neural networks is proposed.
为了改善学习速率，提出了一种确定复数神经网络初始权值的新颖方法。
Finally, the MBP algorithm is compared with the standard BP algorithm. The results shown that the learning speed of MBP algorithm is increased greatly.
最后将标准BP算法和MBP算法进行了比较，仿其结果表明：MBP算法的学习次数和收敛速度得到极大改善。
Because the initialized weights are optimized, the training accuracy and the learning speed are improved a lot for training complex-valued neural networks.
初始权值的优化，使得该算法可以有效地提高复数神经网络的训练速度和计算精度。
Experiment shows neural network classifier that is optimized by algorithm could not only have fast learning speed but also ensure accuracy of classification.
实验结果表明：算法优化后的神经网络分类器不但学习速度快，还能保证分类精度。
The simulation result indicates that the algorithm has much faster learning speed and more superior learning precision compared with the standard BP algorithm.
仿真结果表明：该算法比传统的BP算法具有更快的学习速度和更高的学习精度。
RBF neural network provides an effective means for system identification and modeling with its advantages of smaller calculation quantity and high learning speed.
RBF神经网络以其计算量小，学习速度快，不易陷入局部极小等诸多优点为系统辨识与建模提供了一种有效的手段。
First, this paper investigates the effect of initial weight ranges, learning rate, and regularization co-efficient on generalization performance and learning speed.
首先研究了初始权值的范围、学习率和正则项系数对泛化性能和学习速度的影响。
It is shown in the experiment that this object function converges rapidly, and could improve both the learning speed and robustness of such speed servo control system.
通过实验研究，表明该方法收敛速度快，学习能力强，并提高了系统的鲁棒性，证实了其在电机速度控制中的有效性。
Based on analysing characteristics of error curved surface of the network, the authors have advanced the rapid BP algorithm, which can greatly raise the learning speed.
通过分析网络误差曲面特征，提出了快速BP算法，它可以大幅度地提高学习速度。
Based on analysing characteristics of error curved surface of the network, the authors have advanced the rapid BP algorithm, which can greatly raise the learning speed.
通过分析网络误差曲面特征，提出了快速BP算法，它可以大幅度地提高学习速度。
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