hidden layer
英 [ˈhɪdn ˈleɪə(r)]
美 [ˈhɪdn ler]
隐含层
双语例句
- This paper analyzed the tertiary industry added value in Inner Mongolia using Artificial Neural Network. Finally, the paper built the BP Neural Network model with single hidden layer.
文章通过运用神经网络的建模方法对内蒙古自治区第三产业增加值进行分析，最后建立了单隐层的BP神经网络模型。 - BP neural network topology, including the input layer ( input), hidden layer ( hide layer) and output layer ( output layer).
BP神经网络模型拓扑结构包括输入层（input）、隐层（hidelayer）和输出层（outputlayer）。 - 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。 - The algorithm employs a new method to compute hyper planes to pide the training points into distinct areas so that the hidden layer of neural networks is correspondingly constructed.
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。 - This model can overcome the shortcomings of BP networks, such as the shortcoming of the parameters of network, the uncertain unit number of the hidden layer, and the slowly learning rate, etc.
该模型克服了传统BP神经网络参数不足、隐含层单元数目难以确定、收效速度较慢等缺点。 - This paper proposes a method which can determine the suitable structure in the hidden layer of a neural network.
提出一种确定神经网络隐层中合理结构的方法。 - The effects of neuron number in hidden layer and momentum parameter on classification have been investigated.
文章还对隐含层神经元数目和动量参数的影响做了考察。 - The analysis shows that: by the sacrifice of compression ratio, the quality of the recovered image can be improved with the increase of the number of the neurons in the hidden layer.
分析结果表明：可以通过牺牲压缩率，增加隐含层的神经元数来提高重建图像的质量。 - With the best polynomial approximation as a metric, the rate of approximation of the neural networks with single hidden layer to a continuous function is estimated by using a constructive approach.
以最佳多项式逼近为度量，用构造性方法估计单隐层神经网络逼近连续函数的速度。 - Firstly some hidden layer nodes are initialized, and train the network using competitive learning algorithm.
初始化一定数量的隐层节点，利用竞争学习算法对网络进行学习。 - The method determining the number of hidden layer node is mainly studied in this paper.
重点探讨了隐含层节点数的确定方法。 - Determining the thickness of "hidden layer" is an important problem in engineering geophysics and engineering geology.
确定隐蔽层的厚度，是工程物探、工程地质和工程地震学中的一个重要问题。 - To overcome the disadvantages of traditional neural network, the hidden layer structure and leaning errors are optimized.
为克服传统人工神经网络的缺点，对一般神经网络进行了改进，得到精度较高的进化神经网络。 - The genetic algorithms and the self-construct study of hidden layer nodes were adopted to optimize the NN structure.
并采取遗传算法、网络隐含层节点自构性学习等办法优化网络构造。 - And we presented a practical and valid method of redundancy of hidden layer neurons to gain fault tolerance.
并提出了针对一般前向神经网络的实用的隐层神经元容错方法，这种方法可以有效地提高网络在普遍故障下的容错能力。 - We have studied that the variable structure neural network for single hidden layer and multi hidden layer.
先后研究了神经网络单隐层和多隐层的变化情况。 - It solve the question of hidden layer learning rule that utilize the method of error back propagation.
其采用误差反传的特性解决了隐层引入以后的学习问题。 - Then, determined the number of BP network hidden layer and nodes based on experience piece-try method.
随后采用经验法和凑试法相结合，确定了BP网络隐含层的数目和节点数。 - Then determine the structure of BP neural network layer ( the input layer, hidden layer and output layer, each layer) the number of nodes, thereby completing the structure of BP neural network model design.
随后确定了BP神经网络结构的层数（输入层、输出层、隐层）和各层的节点数，从而完成了BP神经网络结构模型的设计。 - We investigated the wavelet network performance and the problems related to its modeling, and presented three methods to select the wavelet nodes of the network hidden layer with theoretical analysis and algorithms.
研究了小波神经网络性能和建模需要面对的问题，实现了选择网络隐层小波节点的三种方法，并做了相关理论分析。 - The problem of the number of hidden layer and the selecting of neurons nodes of the fault diagnosis model base on BP artificial neural network are analyzed, and the function of the complex network structure is used to optimize network structure of the fault diagnosis model.
分析了基于BP人工神经网络的故障诊断模型隐层个数和隐层神经元节点数的选取问题，并采用网络结构复杂度函数对故障诊断神经网络结构进行优化。 - The number of hidden layer neurons is calculated in case of different features.
然后，计算了不同的特征情况下的隐层神经元数目。 - It not only resolves the previous forecast only fixed network structure and use heuristics to select the number of hidden layer problem ahead, but also selective input of high-dimensional feature, implements the effect of reducing original input dimension.
它不但解决了以前预测时只能提前固定网络结构和采用试探法选择隐层个数的问题，而且还能对高维特征进行选择性输入，实现了对原始输入的降维作用。 - This paper improves traditional three-layer BP neural network classifier. A hidden layer was added and related parameters were improved.
改进了传统的三层BP神经网络分类器，增加了一个隐藏层，并改进了相应的参数。 - Then the design principle, method and implementation process when neural network is using in the prediction of soil water content is elaborated, focusing on input/ output layer, method for determining the number of hidden layer nodes of the neural network. 4.
详细阐述了神经网络应用于土壤含水量预测过程中的设计原则、设计方法和实现过程。重点论述了用于土壤含水量预报的神经网络的输入/输出层、隐含层节点数的确定方法。 - 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。 - It was simulated by Matlab software, and analyzed the effects of the number of hidden layer neurons and different hidden layer activation function to the model prediction accuracy.
通过Matlab软件进行了仿真，并分析了选择不同隐含层神经元个数和不同隐含层激励函数对模型预测精度的影响。 - The ELM has been widely used as a fast learning method for feedforward networks with a single hidden layer.
对于带有单隐层的前馈神经网络，ELM作为一种快速学习算法被广泛使用。 - For the RBF neural network, the focus is to determine the structure of hidden layer.
对于RBF神经网络，其重点是确定隐层结构。