1) hidden nodes
隐层节点
1.
This paper taking one-hidden layer combined BP network model used in the fault diagnosis of power transformer as a exapmle and on the based of training flow process chart, points out that the number of hidden nodes, initial weight values, training error, the max training times and the order of training samples have different influence on the BP network generalization.
以单隐层的BP组合神经网络在基于DGA的电力变压器故障诊断中的应用为例,在BP网络训练流程图的基础上,分别举例阐述了隐层节点个数、初始权值、训练误差、最大训练次数以及训练样本次序对网络训练效果和泛化能力的影响。
2) the number of hidden nodes
隐层节点数
1.
Aiming at the shortcoming of determining the of hidden nodes of LSRBF neural networks based on the imputting information of training data, in paper, the modified monotone index method based on training data with the whole inputting and outputting information is presented to determine the number of hidden nodes of LSRBF neural networks.
针对仅利用训练样本输入信息进行非监督聚类来确定LSRBF神经网络隐层节点数存在的不足,本文提出了基于训练样本输入输出全部信息的修正单调指数法来确定LSRBF神经网络隐层节点数。
2.
In this paper,the new method is presented to determine the number of hidden nodes of RBF neural networks,it makes use of the whole inputting and outputting information of training samples to establish similar matrix of samples,then to determine the number of hidden nodes of RBF neural networks by maximal matrix element method.
针对基于训练样本输入信息进行非监督聚类来确定RBF神经网络隐层节点数的方法存在利用信息不充分的缺陷,该文提出了一种新的确定RBF神经网络隐层节点数的方法。
3) Number of the Hidden layer nodes
隐含层节点数
4) self configuring nodes of hidden layer
隐层节点自构形
5) number of hidden nodes
隐层节点数确定
6) hidden nodes
隐节点
1.
the total number of hidden nodes in the entire network will be small ifthe OINN is used to classify such samples.
以分析输出ＯＩＮＮ为基础、提出了分析前向神经网络隐节点数的线性方法，这种方法得出的隐节点数虽然只是充分条件，不是必要条件，但是得到的结果大大减少了网络不必要的隐节点数。
2.
In this paper, It proved that the hidden nodes of a three-layered feed forward neural network with N-1 hidden units, through adjust the weight Wij and bias bi, it s outputs is linear-independent.
本文首先证明了具有N-1隐节点的三层前馈神经网络,可以通过调整权值和阈值,使得隐节点输出线性无关,以任意精度逼近N个训练样本对。
补充资料：层层加码
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参考词条