该模型包括输入层、隐含层和输出层。
This model includes the input layer, hidden layer and output layer.
输出层参数是复相陶瓷中各组分的体积分数。
The parameters of the output layer are volume percentages of each composition in the multiphase ceramics.
网络模型由三层构成：输入层、隐含层、输出层。
The network model consists of three layers: the input layer, the hidden layer, and the output layer.
输出层的训练是采用基于LMS的监督式数学模型。
The training of the output layer is the supervised algorithm based on LMS.
输出层含有一个或多个节点，这些节点产生输出数据。
There are one or several nodal points in the output layer which generate output data.
点击保存输出层的标签，这样您，将会在这一层的轨道只。
Click on the Keep-Out layer TAB so you will be placing the tracks on this layer only.
使用pull方法则无法知道节点究竟位于输入层、隐藏层还是输出层。
With the pull methodology, individual nodes have no way of knowing if they are in the input, hidden, or output layer.
我们选择的网络结构包括一个输入层、一个隐含层、一个输出层。
The chosen neural network architecture consisted of one input layer, one hidden layer and the output layer.
通过对字符特征的分析，确定输入层，隐含层，输出层单元数目。
Through the analysis of character features, confirm the input layer, hidden layer and the License of output layer units.
网络学习结束后，得到输入层、中间层和输出层各单元的连接系数矩阵。
After studying network, coefficient matrix of each unit which includes input layer, intermediate layer and output layer was gained.
直到满足某条件或者调用输出层将输出发射到外部环境前，这一过程一直持续。
The process continues until a certain condition is satisfied or until the output layer is invoked and fires their output to the external environment.
BP神经网络模型的输入层设12个结点，输出层设22结点，设一层隐含层。
The model consists of three neuron layers: input layer with 12 nodes, output layer with 22 nodes and hidden layer.
该神经网络为3层前向网络，其隐含层神经元数为10个，输出层神经元数1个。
There are 3 layers in multi-layer front neural network, there are 10 nodes in hide layer, and is 1 node in the output layer.
径向基函数神经网络的隐含层到输出层的线性连接权值，则是由最小二乘法来计算得到的。
The connection weights between hidden layer and output layer are got by Least Mean Square Algorithm.
网络隐层-输出层的权值采取最速下降法学习，输入层-隐层的权值采用遗传算法进行学习；
The learning method of hidden-output layer weights is the steepest descent method and the one of input-hidden layer weights is genetic algorithm(GA) .
隐层神经元完成对过程序输入信息的模式匹配和对时间的聚合运算，输出层对输入模式作出响应。
The neurons of hidden layer perform the pattern matching of process input information and aggregation operation of time and respond to the input patterns.
R BF网络的设计问题就是关于网络隐节点数和隐层节点RBF函数中心、宽度和隐层到输出层的权值的性能指标的最小化问题。
The RBF network configuration is formulated as a minimization problem with respect to the number of hidden layer nodes, the center locations and the connection weights.
基于输出层函数为线性函数的三层前馈神经网络，结合自适应步长和动量解耦的伪牛顿算法及迭代最小二乘法导出了一种混合算法。
On the basis of both adaptive BP algorithm and Newtons method, Quasi Newton algorithm with adaptive decoupled step and momentum (QNADSM) for feed-forward neural networks is derived.
给出一种求解超平面以几何分割训练点的新方法，不仅相应地构造了隐层神经网络，而且使得只需再构造一个输出层网络便可实现训练样本所描述的映射。
The algorithm employs a new method to compute hyper planes to divide the training points into distinct areas so that the hidden layer of neural networks is correspondingly constructed.
写数据则需要打开一个输出数据源并在其内创建一个数据层。
Writing data requires opening an output data source and creating a data layer within it.
调试输出消息能够帮助您了解ssl层到底发生了什么问题。
The debug output message can help you to know what exactly happens at the SSL layer.
协议层使用相应的接口队列将数据输出到接口。
The protocol layer outputs data to the interface using the respective interface queues.
表示层是从服务器到客户机的接口，它负责控制输出的格式，让输出可以在客户机上显示。
The presentation tier is the interface from the servers to the client, and is responsible for formatting the output so that it can be displayed on the client.
由于其复杂的结构，你可以对活动递归地进行分组并对流进行连接以形成更高的层，这种层次清晰地定义了输入和输出。
As with complex structures, you can recursively group activities and their interconnection flows into higher-level activities with clearly defined inputs and outputs.
比如说，您可以将各节点的输出嵌入到一个图像文件中并将其存储在Web服务器上，以供网络的下一层获取。
For example, you could have the output of each node embedded in an image file and stored on a Web server, ready to be fetched by the next hop in the network.
多数web层的测试框架遵循黑盒测试方式，开发者用web组件编写测试类来验证渲染的HTML输出是否符合预期。
Most of the web-tier testing frameworks follow black-box testing approach where developers write test classes using the web components to verify the rendered HTML output is what is expected.
为PDF输出添加一个xsl定制层。
为PDF输出添加一个xsl定制层。
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