I do know that you can setup nodes to have explicit connections only as well as have hidden nodes, etc.
我知道你可以设置节点只有明确的连接，以及有隐藏的节点等。
The Fuzzy Pattern Recognition Model developed by Chen Shouyu is used as the stimulation function of hidden nodes.
模型的激励函数采用了模糊模式识别模型。
Such as: existing partial minimum, the slow convergence, selecting the number of hidden nodes difficult and so on.
如：训练时易陷入局部极小、收敛速度慢、隐结点个数难以确定等。
For the commonly used three-layered neural network, how to select the number of the hidden nodes is always a real problem.
对于常用的三层结构的神经网络，隐节点数目的确定一直是个难题，至今无定论。
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。
In this research, various number of hidden nodes of neural network is studied to improve the training result and identification capability of BP neural network.
本文在使用BP神经网络对自相关过程进行监控的基础之上，对隐层神经元数对于神经网络训练收敛性及识别率的影响进行分析研究。
The program has a wrapper that deduces how many input nodes (count and target) are needed, based on the actual input file. Choosing the number of hidden nodes is trickier.
这个程序有一个包，它能够根据实际文件推断出需要多少输入节点（计算在内的和期望的），选择隐藏节点的数目是一个诀窍。
Then, a method is presented to compress the number of hidden nodes, which can be extended to more than three layer perceptrons and to the case of using different activation functions.
然后，给出了一种可对上述三层感知器进行压缩的隐节点的压缩方法，它可以推广到三层以上的感知器和节点激发函数不同的情形。
BP algorithm is often used to correct weights of neural network because number of hidden nodes, studying speed and generation ability of neural network are related to activation function.
以往的BP算法调节神经元网络的权值，其网络的隐层结点数、网络学习快慢程度及网络的泛化能力都与网络的激励函数有关的。
The output of each input layer node is fed to each of six hidden-layer nodes, which in turn feed three output nodes.
再将各个输入层节点的输出 提供给6 个隐藏层节点，这些隐藏层节点将依次提供给3 个输出接点。
With the pull methodology, individual nodes have no way of knowing if they are in the input, hidden, or output layer.
使用pull方法则无法知道节点究竟位于输入层、隐藏层还是输出层。
The RBF network configuration is formulated as a minimization problem with respect to the number of hidden layer nodes, the center locations and the connection weights.
R BF网络的设计问题就是关于网络隐节点数和隐层节点RBF函数中心、宽度和隐层到输出层的权值的性能指标的最小化问题。
First, the usual ways that are employed to choose the number of RBFNN's hidden layer nodes are analyzed and compared.
首先对目前常用的RBF网络的隐层节点数的选择办法进行了分析，并指出它们的优点和不足。
As for it, by improving learning algorithm of traditional RBF neural network, a new dynamic cluster-based self-generated method for hidden layer nodes is proposed.
对此，本文改进了R BF神经网络的学习算法，提出了一种基于聚类的动态自生成隐含层节点的思想。
The main problems in designing a RBFNN depend on fixing the nodes of the hidden layer, the parameters of the centers and the linear weights.
设计中存在的主要问题包括隐层神经元数、中心和半径的确定，以及网络权值的训练。
Here I give some references to the selection of the number of hidden-layer nodes and learning rate.
在这里我们对于隐含层节点数选择、学习速率选择等问题提出一些参考意见。
The model consists of three neuron layers: input layer with 12 nodes, output layer with 22 nodes and hidden layer.
BP神经网络模型的输入层设12个结点，输出层设22结点，设一层隐含层。
He pointed out that despite the AIDS virus in the blood has disappeared, but they are still hidden in the human body, spleen, lymph nodes and other parts of nerve cells.
他指出，尽管血液中的艾滋病病毒已不见踪影，但它们仍然潜伏在人体的脾脏、淋巴结和神经细胞等各部位。
Provided with the Algorithm to determine the pre-possessing of sample data, learning rate, momentum factor, the Number of Hidden Layer Nodes, etc.
给出了样本数据的预处理以及学习因子、动量系数、隐含层结点数等诚然定方法。
Provided with the Algorithm to determine the pre-possessing of sample data, learning rate, momentum factor, the Number of Hidden Layer Nodes, etc.
给出了样本数据的预处理以及学习因子、动量系数、隐含层结点数等诚然定方法。
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