重点探讨了隐含层节点数的确定方法。
The method determining the number of hidden layer node is mainly studied in this paper.
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。
Here I give some references to the selection of the number of hidden-layer nodes and learning rate.
只是对于小的损伤识别精度相对差一些，可以通过增大网络隐含层节点数或网络层数来加以解决。
Yet with little damage the result is relatively low. To resolve the problem, augmenting node of hidden layer or number of hidden layer.
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。
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.
同时采用基于优化原理的HCM算法实现聚类过程，来确定R BF网络的隐含层节点数，使网络的利用效率较高。
Meanwhile optimization theory based HCM clustering algorithm is used to cluster sample data to determine the number of node of hidden layer, so that the efficiency of RBF network in use is high.
神经网络采用改进的BP网络，提出的一种确定神经网络最优隐含层节点数的新方法，其正确性得到了大量事实的验证。
The neural net adopts the improved BP net, a new method of determining the node number of optimum implication layer of neural net, and its correctness has been proved by a lot of facts.
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.
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.
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