| 1. | The most invasive change in the 2 . 6 kernels is a fundamental change of the input layer
2 . 6内核最扰人的改变是输入层的低级变化。 |
| 2. | The network consists of three layers : the input layer , the hidden layer , and the output layer
文中的bp网络模型都是由三层构成：输入层、隐含层、输出层。 |
| 3. | 5 . 2 . 2 mouse configuration again because of the changes in the input layer , you may have to reconfigure the x window system and
还是由于输入层的变化，如果您的鼠标在2 . 6内核下不工作，您可能得重新配置x window system和 |
| 4. | The complex - valued weights between hidden and output layer are updated by solving linear system based on finding the complex - valued weights between input and hidden layer
当输入层和隐层之间的权值计算出来后，就可以通过求解线性方程组得到隐层和输出层之间的权值。 |
| 5. | The percentage of dwelling units in high - rise and low - rise buildings is entered . this affects the selected ppf if an area average was selected
输入层数较多和层数较少的楼宇内住宅单位所占比率，倘若选取的是面积平均值，这个比率数字将对所选取的每单位人口有所影响。 |
| 6. | In nntcs , we use artificial neural networks ( ann ) as the classifier . the recorded term frequencies form the original feature vector , matching with neurons in the input layer of ann one by one
系统使用神经网络作为分类器，特征词的词频组成原始特征向量，和神经网络输入层的神经元一一对应。 |
| 7. | In the first case you will not be affected by this issue . 5 . 2 . 2 mouse configuration again because of the changes in the input layer , you may have to reconfigure the x window system and
再一次地，因为输入层的改变，如果您在升级至2 . 6的kernel后滑鼠无法正常运作的话，您也许得要重新设定x window system及 |
| 8. | The network has four layers . input layer has 16 nodes . the first hidden layer has 17 nodes ; the second hidden layer has 10 nodes and one output node . 37 projects " data is used in training samples
网络共有四层，输入层节点数为16个，隐含层一的节点数为17个，隐含层二的节点数10为个，输出层节点1个。 |
| 9. | This paper describes a three - layer feedforward rough neural network which has four input rough neurons and ten input conventional neurons , five hidden rough neurons and one output rough neuron
本文给出了三层前向粗神经网络，输入层由4个粗神经元和10个一般神经元组成，隐层和输出层分别由5个、 1个粗神经元组成。 |
| 10. | Keeping the original structure of cbp unchanged , we add an extra node to cbp input level and assign specific values to the weights between that node and the hidden level , and then we obtain a more general model than cbp , which is icbp
我们在保持cbp原有结构下，通过对cbp的输入层扩充节点( 1个)的特殊构造，以及对该节点与隐层间权值的特殊赋值，获得了较cbp更一般的改进网络模型( icbp ) 。 |
