Using this utility, you explored TCP and udp workload tuning while also learning some other noteworthy parameters.
使用这个实用工具，您研究了tcp和udp工作负载优化，同时还了解了一些其他值得关注的参数。
Then some parameters of the controller are modulated by hybrid learning algorithm of ladder descent (LD) and least square error (LSE) so as to attain better control precision.
然后通过梯度下降法和最小二乘法相结合的混合学习算法，对控制器参数进行调整以提高其控制精度。
After learning about this upgrade impact, the system catalog changes, new ONCONFIG parameters, and reversion impact, you are now ready to take full advantage of all the new features in version 11.70.
学习了这些升级影响、系统目录变更、新onconfig参数、以及降级影响之后，现在您就可以充分利用Version 11.70中的新功能了。
Through learning and remembering the adjusting rule of PID parameters, the PID parameters are adjusted on line by the network.
该网络通过学习并记忆PID参数调整规则，实现了在线调整PID参数。
Learning rules are constructed according to deterministic annealing to optimize classifier parameters, on purpose to reduce classification error and system entropy of the space to be identified.
由确定性退火技术构造学习规则用于优化分类器参数，目的是减少分类误差以及待识别空间的系统熵。
This paper also develops a fuzzy competitive learning scheme for these new reference vector parameters, and applies the algorithm to the difficult task of clustering documents.
针对新的参考向量开发了模糊竞争学习模式，并用该算法成功地解决了文献聚类的难题。
There exist over learning and the difficulties of selecting suitable parameters when training neural network.
在神经网络的训练当中存在“过学习”现象以及参数难以选择的困难。
The second layer accumulates the responses of these local nodes, weighted by the learning mixing parameters.
第二层计算局部节点的加权响应和，混合参数作为学习加权。
A learning algorithm of subtractive clustering for RBF network is used to obtain the parameters of radial basis function so as to optimize network structure.
在RBF网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数，使网络结构得到优化。
A learning algorithm of subtractive clustering method for RBFNN is used to obtain the parameters of radial basis function, so that RBFNN has an optimized structure.
在RBF神经网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数，从而使神经网络结构得到优化。
In WNN the most fast grads descent methodology was adopted to adjust the network parameters and the learning rate by self adapting learning rate method.
对小波神经网络采用最速梯度下降法优化网络参数，并对学习率采用自适应学习速率方法自动调节。
Self-generating neural network (SGNN) is a self-organization neural network, whose network structures and parameters need not to be set by users, and its learning process needs no iteration.
自生成神经网络（SGNN）是一类自组织神经网络，它不需要用户指定网络结构和学习参数，而且不需要迭代学习，是一类特点突出的神经网络。
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。
Through adjusting weight, computing error rate and modifying the parameters of hidden nodes, optimal results will be achieved in the learning procedure.
学习过程通过调整权值、计算误差、修正隐层单元的参数，以达到最优结果。
The parameters of me fuzzy control rules of me controller can be learned by the learning slgorithm of the neural netowrk. and the inference process can be realized by the network.
应用单层神经网络可以学习多变量模糊控制规则中的未知参数．还可由它来实现多变量模糊推理过程。
Because of defects of BP algorithm, a hybrid learning algorithm is applied to train and optimize the network parameters.
针对BP算法的不足，使用混合学习算法训练网络，优化了网络参数。
After learning how the morph daemon operates and which parameters we need to edit to control it, we'll add in our magic daemon.
在学习如何经营和守护变形的参数，我们需要编辑来控制它，我们将加入我们的魔法守护进程。
These results suggest that the alterations of learning and memory behavior are closely associated with that of synaptic structural parameters of brain in natural aging mice.
结果提示，衰老过程中，小鼠学习记忆行为与其脑内突触结构参数变化密切相关。
The network is used to remember adjusting rules of PID parameters by learning so the network can adjust PID parameters on line by rules.
该网络通过学习记忆PID参数调整的基本规则，实现了PID控制器参数的在线调整。
The dynamic optimization of learning parameters can adjust learning parameters dynamically and select optimal learning parameters.
随后，动态优化学习参数算法动态地调整和选取优化的学习参数。
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。
Next the general method of applying fuzzy ARTMAP model to feature level fusion is also expounded and we put forward a learning algorithm with adaptive vigilance parameters for each cluster.
继而研究了模糊artmap网络用于特征层融合识别的方法，并提出了一种网络警戒参数自适应调整新算法。
It can not only reduce the network learning cycle, but also optimize the network structure by using Kalman filter to adjust of the parameters of the neural network.
利用卡尔曼滤波调整神经网络的参数，不仅可以减少网络的学习周期，而且可以优化网络的结构。
First, the fuzzy space of input variables is partitioned by means of on-line fuzzy competitive learning. Further, the parameters of fuzzy model are estimated by means of Kalman filtering algorithm.
首先，利用在线模糊竞争学习方法划分输入变量的模糊输入空间，然后利用卡尔曼滤波算法估计模糊模型的参数。
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 learning of Bayesian Networks is an important tache, which combines training data with prior knowledge and model evaluation to acquire the structure hidden in data and parameters.
贝叶斯网络的学习是数据挖掘中非常重要的一个环节，是将先验知识和模型评价融入训练数据，获得数据中隐藏的拓扑结构和参数的过程。
Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN.
此外，退火的鲁棒学习算法，提出这些隐藏的节点参数以及SVR - NN的权重的调整。
Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN.
此外，退火的鲁棒学习算法，提出这些隐藏的节点参数以及SVR - NN的权重的调整。
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