| 1. | Step 2 : hidden nodes calculate their outputs
第2步：隐藏节点计算它们的输出 |
| 2. | Lastly , it isn t clear how this applies to nets with hidden nodes
最后，这不一定适用于存在隐藏节点的网络。 |
| 3. | For source code recognition , 6 to 8 hidden nodes seem to work very well
对于源代码的识别， 6到8个隐藏节点似乎工作得很好。 |
| 4. | Fuzzy cluster analysis method for determining the number of hidden nodes of feedforward neural networks
确定前向神经网络隐层节点数的模糊聚类分析法 |
| 5. | Is an algorithm that extends the analysis that underpins the delta rule to neural nets with hidden nodes
这一算法把支持delta规则的分析扩展到了带有隐藏节点的神经网络。 |
| 6. | The network can approximate any continuous function with any degree of accuracy provided its hidden nodes is as many as enough
只要隐层节点数足够多，网络就可以以任意精度逼近任意连续函数。 |
| 7. | 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。 |
| 8. | Where d for a hidden node n , turns on how much n influences any given output node ; and how much that output node itself influences the overall error of the net
其中d ( n )是隐藏节点n的函数，让我们来看（ 1 ） n对任何给出的输出节点有多大影响； （ 2 ）输出节点本身对网络整体的误差有多少影响。 |
| 9. | 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。 |
| 10. | Limiting ourselves to nets with no hidden nodes , but possibly having more than one output node , let p be an element in a training set , and t be the corresponding target of output node n
将我们的网络限制为没有隐藏节点，但是可能会有不止一个的输出节点，设p是一组培训中的一个元素， t ( p , n )是相应的输出节点n的目标。 |
