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。
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。
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。
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。
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。
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。
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。
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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