• 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 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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  • There are one or several nodal points in the output layer which generate output data.

    输出层含有一个或多个节点,这些节点产生输出数据。

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  • The connection weights between hidden layer and output layer are got by Least Mean Square Algorithm.

    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。

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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 parameters of the output layer are volume percentages of each composition in the multiphase ceramics.

    输出层参数是复相陶瓷中各组分的体积分数。

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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 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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  • 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 learning method of hidden-output layer weights is the steepest descent method and the one of input-hidden layer weights is genetic algorithm(GA) .

    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;

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  • 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) .

    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;

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