output layer

the training of the output layer is the supervised algorithm based on lms.

输出层的训练是采用基于LMS的监督式数学模型。

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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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its structure was divided into calculating input fuzzy membership degree layer, every rule application degree layer, normalization of application degree layer, every rule output layer and fuzzy system output layer.

其结构分为计算输入的模糊隶属度、每条规则适用度、适用度归一化、每条规则输出及模糊系统输出5层。

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and a tree crown recognition system was established in which there are 15 nodes of tree crown features and image fractional dimensions in the input layer, 6 nodes of tree crown types in the output layer and 1 node in the implicit layer.

该系统的输入层分别是图像的维数特征和树木形状特征值，共计15个节点，一个隐含层，输出层则是要判断的6种树形。

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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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In the output layer, the neuron production auto-attenuates at a definite speed too.

另外输出层的每个神经元也具有负反馈功能.

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this is because the only parameters that are adjusted in the learning process are the linear mapping from hidden layer to output layer.

这是因为唯一在学习过程中被调整过的参数是那此从隐藏层到输出层的线性映射。

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a radial basis process neural networks model was proposed, which is a kind of three-layer forward structure constituted of input layer, radial basis function hidden layer and output layer.

提出了一种径向基过程神经元网络，该网络模型为3层前向结构，由输入层、径向基过程神经元隐层和输出层组成。

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based on input layer, latent layer and output layer correlation rule, the structure learning method of stochastic fuzzy neural network considered the effects on the latent layer function, which was very important for engineering applications.

基于输入层、隐层、输出层相互关系准则函数的随机模糊神经网络结构学习算法，综合考虑了输入、输出信号对隐层函数的影响。

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a three-layer architecture which consists of an input layer, a hidden layer and an output layer, is adopted in our neural network.

本文采用包含输入层，隐藏层和输出层的三层式神经网络结构。

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A three-level B-P neural network that can alert learning rate and momentum coefficient automatically was taken to do it. There were 6 nods in input layer, 8 in hide layer and 2 in output layer.

改进后的B-P网络为3层网络，输入层6个结点，隐形层8个结点，输出层2个结点，并且可以在线调整学习速率η。

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by analysing the relevance between hidden layer neurons and output layer neurons of bp neural network, we can determine the number of neurons in hidden layer according to the size of the correlation, so that achieve the purpose of optimizing neural network structure.

通过分析BP神经网络的隐含层神经元与输出层神经元的关联程度，再根据关联度的大小确定网络隐含层神经元的数目，以达到优化神经网络结构的目的。

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weights in the output layer are gained by using the pseudoinverse algorithm.

输出层的权值使用伪逆算法确定。

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Output layer to flush itself automatically after every output block.

将使输出层，在每段信息块输出后，自动刷新。

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this paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of bp neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer.

本文指出了目前传统的数理统计在股票分析上的不足，使用BP神经网络算法对股市进行预测，通过建立一个三层结构的神经网络，即输入层、隐含层和输出层。

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the transformation from input layer to hidden layer is nonlinear and that from hidden layer to output layer is linear.

输入层到隐层的变换是非线性的，隐层到输出层的变换是线性的。

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this algorithm reduces number of hidden nodes and enhances the bp network convergence speed by re-selecting activation function and adjusting weight value of neurons transformation function, scaling coefficient and displacement parameter in output layer and hidden layer.

通过重新选取神经元的激活函数，对输出层和隐层中神经元转换函数的权值、缩放系数和位移参数进行调整，减少隐层节点数，加快BP网络的收敛速度。

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By introducing the hidden representation or hidden variables into RNN, training the complicated RNN is decomposed into training a set of single neurons and a linear output layer.

利用隐含观测量，将复杂RNN的训练分解为线性输出层和多个单隐元的参数估计。

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Whereas, the initial network structure of CC algorithm is merely composed of input layer and output layer and the hidden neural cells are inserted into the network one by one in the training process.

CC算法仅要求初始网络含有输入层和输出层，通过运算不断向网络增加隐含节点，从而最大限度的减少了在网络构建过程中的主观因素。

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a learning algorithm was introduced, in which most connection weights of the network are fixed, only those between the output layer and the last hidden layer are needed to be adjusted.

给出了网络的学习算法，网络的大部分权值都是固定的，只有输出层与最后隐层之间的权值需要调节。

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The self-organization process neural networks ( FPNN ) consists of input layer and competition layer and output layer.

自组织过程神经元网络 ( FPNN ) 由输入层、竞争层和输出层组成.

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the output layer realizes the integration of the rules;

输出层实现规律的综合；

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in this method, a multilayer feedforward neural network model with strong mapping, associating, generalizing and summarizing abilities which includes input layer, middle processing layers, output layer and training layer, is established.

在该方法中建立了由输入层、中间处理层、输出层和训练层构成的，且具有很强映射、联想、推广和概括能力的多层式前向神经网络模型。

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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.

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BP neural network is adopt three-layer structure, including input layer, hidden layer and output layer. A lot of characters are trained according to the standard BP neural network steps including activate computing, error calculation, connection weights adjustment and threshold adjustment.

采用三层结构，包括输入层、隐含层、输出层。根据标准的BP神经网络激活运算、误差计算、连接权调整、阈值调整等步骤，来训练大量字符。

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the evaluation model includes five parts: input layer, subject function layer, rule layer, unitary layer, output layer.

该评价模型包括输入层、隶属函数层、规则层、归一层和输出层。

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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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this approach trains the output layer and the hidden layer separately, and improves an existing modified backpropagation training algorithm [ 28].

这种算法是在文献中的算法基础上进行的改进，它对输出层和隐层分别进行训练。

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the output layer type of this neural network is softmax.

这个神经网络的输出层类型是softmax。

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