Besides, its simple and regular topology make it easy to synthesize the neural networks, especially for the hidden neuron, and suitable for the optical implementation.
另外，洗牌型全互连神经网络整齐、简单的结构方便了网络的综合，特别是网络隐单元的综合，并且十分适合于神经网络的光学实现。
The principle and methods to determine the network parameters such as number of neuron in hidden layer, excitation function and the convergence accuracy have been analyzed in detail.
并且详细叙述了神经网络结构参数如隐含层神经元个数、激励函数、网络收敛精度等的确定原则和方法。
In this scheme, the inputs of hidden layer neurons are acquired by using the gradient descent method, and the weights and threshold of each neuron are trained using the linear least square method.
在该方案中，通过梯度法获取隐层神经元的输入，使用线性最小二乘法训练各神经元的权值和阈值。
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。
Compare to other ANN modeling, the neuron number of the CNN hidden layer is few, and the ability of generalization of the CNN system is well.
与其它的AN N建模相比较，用CNN建立的模型的隐层神经元数量少，系统的泛化能力强。
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。
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。
应用推荐