| 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. | As to the mix pixels , we construct a bp neural network which the nodes of input layer are the bands of remote sensing and the nodes of output layer are percent of several kinds of object
另外还构造了一个用于混合像素分类的神经网络，输出层节点为各典型地物类别所占的百分比。 |
| 5. | 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。 |
| 6. | 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 |
| 7. | 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。 |
| 8. | Based on the test data of 61 steel reinforced concrete columns , a model with 5 input layers , 6 implicit layers and 1 output layer ( 5 - 6 - 1 ) is developed to analyze the influence of various parameters on displacement ductility by the principle of back propagation ( bp ) neural network
摘要通过对61根型钢混凝土柱试验数据的整理，利用神经网络原理建立5 - 6 - 1型反向传播( bp )神经网络模型，分析不同参数对型钢混凝土柱位移延性系数的影响。 |
| 9. | In this paper , to resolve the coupling phenomena between temperature and humidity in wood drying system , a bp neural network based pid controller is proposed and applied to wood drying system . the architecture and learning algorithm of the proposed controller is more simpler and the physical meanings of the input layer ' s neurons and output layer ' s neurons are explicit . based on predefined control rules and self - learning , the bp network changs the scaling integral and differential parameters , therefore is able to control the variants using classical pid control algorithms and at the same time , decoupling control is implemented as well during the control procedure
本文针对木材干燥过程中温、湿度耦合的现象，提出一种将新的基于bp神经网络的pid控制器应用于木材干燥控制系统的方案，其结构和学习算法相对简单，输入层和输出层神经元物理意义明确；它根据设定的某一控制规律，通过网络的自学习，调整pid控制器的比例、积分和微分参数，从而利用经典的pid控制算法得到相应各变量的控制量参与控制，并在该过程中实现解耦控制，而不用给定样本信号进行在线的学习。 |
| 10. | In the process of image compression , considering that the three or more layers bp networks have some redundancies in the weights between input layer and meddle layer so as to effect the network ' s study speed and compression quality , we bring forward a new two layers back propagation networks and it ' s arithmetic
考虑到利用三层及三层以上bp网络对图像压缩，其有效信息是中间层单元上的输出值和中间层与输出层之间的连接权，而输入层与中间层的连接权是冗余的，以至于对学习速度和压缩质量有负面影响。 |
