• This model includes the input layer, hidden layer and output layer.

    该模型包括输入层、隐含层和输出层。

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  • Each pixel's grayscale value is then fed to each of six input layer nodes.

    然后,将每个像素的灰阶值 提供给6 个输入层节点。

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  • The network model consists of three layers: the input layer, the hidden layer, and the output layer.

    网络模型由三层构成:输入层、隐含层、输出层。

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  • The chosen neural network architecture consisted of one input layer, one hidden layer and the 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。

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  • The fuzzy set theory was employed at the input layer to make the processing of linguistic variables possible.

    网络的输入层引入模糊集合理论,使网络能处理语义变量。

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  • The output of each input layer node is fed to each of six hidden-layer nodes, which in turn feed three output nodes.

    再将各个输入层节点的输出 提供给6 个隐藏层节点,这些隐藏层节点将依次提供给3 个输出接点。

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  • The model consists of three neuron layers: input layer with 12 nodes, output layer with 22 nodes and hidden layer.

    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。

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  • Through the analysis of character features, confirm the input layer, hidden layer and the License of output layer units.

    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。

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  • Input layer is respectively the displacement and speed of car, the Angle and Angle speed between pendulum bar and vertical line.

    输入层分别为小车的位移和速度、摆杆偏离铅垂线的角度和角速度。

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  • After studying network, coefficient matrix of each unit which includes input layer, intermediate layer and output layer was gained.

    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。

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  • The first layer is input layer consisting of a group of processing units which are responsible for acceptance of data imported to the network.

    第一层即输入层,包含一组处理单元,负责接受向网络输入的数据。

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  • The traditional 3-layer feed forward BP network modal of morphological opening and closing operation was modified by extending the input layer data.

    采用扩展输入层数据的办法修正了传统的形态开、闭运算三层前馈BP网络模型。

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  • When BP network model is established, 8 parameters affecting the debris flows activity of the studying area are chose as the net input layer, and 20 samples are used to train the network.

    在BP网络模型建立时采用了对研究区泥石流活跃程度影响最主要的8个参数作为输入层,并选取了研究区的20个样本对网络进行训练。

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  • Therefore, the second query is specified for layer 1 in the input panel.

    因此,在输入面板中,第二个查询被指定为第1层。

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  • This function will create a new buffer feature data layer around the input point features at user specified distance.

    此函数将以输入点为基础,按照用户指定的距离创建一个新缓冲区轮廓数据层。

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  • However, there is only one protocol queue, which is called the IP input queue, for the entire protocol layer.

    不过,对于整个协议层,只有一个称为IP输入队列的协议队列。

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  • The interface layer inputs data to the protocol layer through this IP input queue.

    接口层通过此IP输入队列将数据输入到协议层。

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  • This layer consists of a dynamically generated XML specification based on input parameters of the day, month and year.

    这一层包括基于输入参数(日期、月份和年)自动生成的XML规范。

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  • At the input end of the overall system, you simply need to create the relevant input files for the first layer of the network directly.

    在整个系统的输入端,只需为网络的每一层直接创建相关的输入文件。

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  • With the pull methodology, individual nodes have no way of knowing if they are in the input, hidden, or output layer.

    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。

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  • This is a critical part of our layer of goodness on top of Cascading and Haooop that lets us weasel out of dealing with Hadoop input formats.

    这是我们置于Cascading和Haooop之上的那一层东西的关键,可以让我们免于处理Hadoop输入格式。

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  • Following implementation of integration components, the BPM layer provides capture and delivery of event data and statistics, which can also be input back into the modeling environment.

    在实现集成组件后,BPM层将提供事件数据和统计数据(这些数据还可以被输入到建模环境中)的捕获和交付。

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  • The neurons of hidden layer perform the pattern matching of process input information and aggregation operation of time and respond to the input patterns.

    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。

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  • The control of layer thickness by changing energy input. When the powder feed conditions were constant, the layer thickness was increasing with the increase oftemperature of melt pool.

    改变能量输入的层厚控制:送粉条件不变时,熔覆层厚度随熔池温度的增加而增加,通过控制激光功率或扫描速度可以实现熔覆层厚度的反馈控制。

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  • The selected membership function made neural network weight values have definite knowledge meaning, and the input characteristic variables were translated into fuzzy variables by fuzzy layer.

    选取的隶属函数使神经网络权值有一定的知识表示意义,并通过模糊化层将输入特征量转化为模糊量。

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  • Further, according to thermal cycle calculated by the finite element method, the thickness of reaction layer was predicted in the case of different heat input.

    进一步,通过有限元数值模拟方法计算了界面不同位置的热循环曲线,进行了不同热输入条件下反应层厚度的预测。

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  • Secondly, the profile and layer of the damaged member is also determined by probabilistic neural network with input of the normalized damage-signal index.

    然后将构件损伤引起的标准化的损伤信号指标输入概率神经网络,进行损伤构件所在侧面及所在层的判定;

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  • Secondly, the profile and layer of the damaged member is also determined by probabilistic neural network with input of the normalized damage-signal index.

    然后将构件损伤引起的标准化的损伤信号指标输入概率神经网络,进行损伤构件所在侧面及所在层的判定;

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