| 1. | They had at least one source or input node .
它们至少有一个发点或输入节点。 |
| 2. | Every flow contains at least one input node , whose characteristic have been defined
每个流都包含至少一个输入节点，它的特征已做了定义。 |
| 3. | Clearly , one structural difference is that our net has twenty input nodes , but this should not be surprising , since our description has already suggested this possibility
显然，一个结构上的不同是我们的网络有20个输入节点，但这是很正常的，因为我们的描述已经暗示了这种可能性。 |
| 4. | The program has a wrapper that deduces how many input nodes count and target are needed , based on the actual input file . choosing the number of hidden nodes is trickier
这个程序有一个包，它能够根据实际文件推断出需要多少输入节点（计算在内的和期望的） ，选择隐藏节点的数目是一个诀窍。 |
| 5. | ( 4 ) applying the relationships between faults and fealtures in probability causal model , the bp neural network is improved by adding the direct connections between the input nodes and the output nodes
( 4 )利用概率因果模型中征兆与故障之间的关系，对bp网络进行了改进，增加了输入与输出相关节点的直接联接。 |
| 6. | So we establish more reasonable developing model of deflection with neural networks model , which input node include dynamic vehicle load . from the simulating result , neural networks model is suitable to describe the deflection performance
课题结合边缘学科? ?神经网络建立了基于神经网络的路面弯沉发展模型，在模型中将车辆动载作为网络的一个输入结点，反映了动载对路面设计的影响。 |
| 7. | 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。 |
| 8. | The application of rough sets theory and methods in neural network technology is studied . after combined rough sets theory with the neural network technology , the neural network recognition system based on rough set theory is advanced , in which rough sets theory is used to determine the number of neural network ' s input nodes . the complexity of neural network ' s structure is reduced
论文对粗糙集理论及方法在神经网络技术中的应用进行了研究，从而将粗糙集理论与神经网络技术相结合，提出了基于粗糙集理论的神经网络识别系统，利用粗糙集理论来确定神经网络的输入节点数，降低了神经网络的结构的复杂性，给出了该算法的详细步骤。 |
| 9. | Evidence suggests that the prognostic ability of the new model with high stability , when hidden nodes changing nearby input nodes and training times changing at the certain extent , is significantly better than traditional step wise regression model mainly due to the new model condensing the more forecasting information , properly utilizing the ability of ann self - adaptive learning and nonlinear mapping . but the linear regression technique only selects several predictors by the f value , many predictors information with high relative coefficients is not included . so the new model proposed in this paper is effective and is of a very good prospect in the atmospheric sciences fields
进一步深入分析研究发现，本文提出的这种基于主成分的神经网络预报模型，预报精度明显高于传统的逐步回归方法，其主要原因是这种新的预报模型集中了众多预报因子的预报信息，并有效地利用了人工神经网络方法的自组织和自适应的非线性映射能力；而传统的逐步回归方法是一种线性方法，并且逐步回归方法只是根据f值大小从众多预报因子中选取几个预报因子，其余预报因子的预报信息被舍弃。 |
