| 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. | A 3 layer - neural network is used as classifier , the number of nodes in hide layer is determined on trial and error
用前向三层神经网络作为苹果特征的分类器，结合试验结果，用试凑法确定了隐含层节点的个数。 |
| 3. | Aiming at the problem as dormant layer points , a new method named gdann was studied . with the method , the best ann model can be acquired
针对神经网络模型中存在的隐含层结点数难以确定等问题，研究了灰色动态神经网络模型。 |
| 4. | A new way used to decide the neuron ' s number of the hidden layer is proposed based on the analysis and on the experiential way proposed by others
对神经网络隐含层的作用进行分析，在此基础上，借鉴有关文献提出的经验公式，提出了确定隐含层节点数的新方法。 |
| 5. | The network has four layers . input layer has 16 nodes . the first hidden layer has 17 nodes ; the second hidden layer has 10 nodes and one output node . 37 projects " data is used in training samples
网络共有四层，输入层节点数为16个，隐含层一的节点数为17个，隐含层二的节点数10为个，输出层节点1个。 |
| 6. | In this kind of networks , rough neurons locate in the hidden - layer and they consist of three parts which generated by two hyperplanes partition universe . the hyperplanes are obtained by support vector machines
该方法引入多个类似于支持向量机的子神经网络,并将网络中的隐含层单元设计成由多组粗糙神经元构成的网络单元。 |
| 7. | To conclude from the given examples , each model , after spending little time on training themselves from sample data , could assess the damage degree for existing bridges using trained weighted values and thresholds
实例计算表明，各网络模型花费很少的时间完成对样本的训练后，便可利用训练好的隐含层权值与阈值对实际桥梁进行评估。 |
| 8. | Bp model can quite improve the accuracy of pricing result ; 2 . need to confirm the number of the hidden layer of neuron rationally ; 3 . should confirm population size rationally while optimizing ann ; 4
基本结论如下： ( 1 ) bp模型能够提高定价结果的准确度；武汉理工大学硕士学位论文( 2 )建模需要合理确定隐含层神经元数目; ( 3 )优化网络应恰当确定初始群体规模; ( 4 )优化网络需要合理设计交叉算子。 |
| 9. | The aim to study the system structure is to have better understanding , description and control of the complex system . the current studies focus on the system structure in a general way rather than differentiate the inner structure from the analytic structure of a system
结合复杂系统特征和认知的阶段与层次,本文在分析ahp和anp内在作用机制的基础上分别给出了系统分析结构表达的方法即具有隐含层的分析结构。 |
| 10. | In the network , the input node is 64 , the middle is 20 and the output is 4 . we also use matlab train and simulate the designed network . finally , we designed software , which combines all the correlate theory and method list above to validate the thinking
神经网络在汉字识别中的应用包括研bp神经网络及其改进算法、设计汉字识别所需要的bp神经网络，即在神经网络的输入层、中间层、隐含层采用64 ? 20 ? 4的结构，并利用matlab6 . 5对所设计方案进行仿真和验证。 |
