| 1. | 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。 |
| 2. | The neurons in the topological output layer correspond to different textures when the training process is finished
网络的输出层对应的不同纹理的类别，从而将不同模式的纹理归为各自的类别。 |
| 3. | The clustering result shows that the som method ran cluster the regional industrial structure into five categories in the output layer
结果表明，自组织映射的方法能够将地区产业结构在输出层聚成5个区域。 |
| 4. | The full - rank matrix is employed to find the complex - valued weights between hidden and output layers by the least mean square algorithm
利用这个满秩矩阵，通过最小平方算法就可以求得隐层和输出层之间的复数权值。 |
| 5. | The complex - valued weights between hidden and output layer are updated by solving linear system based on finding the complex - valued weights between input and hidden layer
当输入层和隐层之间的权值计算出来后，就可以通过求解线性方程组得到隐层和输出层之间的权值。 |
| 6. | First , the basic concept of nerve net has been introduced . and then the detail structure of the bp nerve net and the detail design precept of input and output layer have been given
首先介绍了神经网络的基本概念，接着给出了神经网络的具体结构和输入输出单元的设计方案。 |
| 7. | As to the mix pixels , we construct a bp neural network which the nodes of input layer are the bands of remote sensing and the nodes of output layer are percent of several kinds of object
另外还构造了一个用于混合像素分类的神经网络，输出层节点为各典型地物类别所占的百分比。 |
| 8. | 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。 |
| 9. | The joint of two algorithms is utilizing chaos genetic algorithm to resolve rbfnn connection node weight of hidden - output layer . similarly simulation has testified the rationality of the modified rbfnn . furthermore , modified rbfnn is adopted to building converter distilling
结合点就是采用混沌遗传算法来求解径向基神经网络隐层到输出层的权值，并且仿真实验验证了改进型径向基神经网络算法的合理性。 |
| 10. | The result shows that the weight values of output layer neurons control the generalization performance of dpfnn . based on this result , a new approach for improving the generalization performance of dpfnn is proposed , which regularizes the output layer weights during dpfnn learning process
在这个结果的基础上，提出了基于输出层权值正则化的强推广性dpfnn学习算法，通过控制输出层权值提高了dpfnn的推广能力。 |
