| 1. | Pattern learning with the back - propagation algorithm
利用反向传播算法的模式学习 |
| 2. | Fuzzy backpropagation algorithms and their convergence
模糊反向传播算法及其收敛性 |
| 3. | The second model is bp of neural network
第二种模型是神经网络反向传播算法模型。 |
| 4. | Moreover , the basic outline of a back - propagation algorithm runs like this
关于反向传播算法的基本情况大致如此。 |
| 5. | With the back - propagation algorithm in hand , we can turn to our puzzle of identifying the language of source code samples
在掌握了反向传播算法后，可以来看我们的识别源代码样本语言的难题。 |
| 6. | ( 3 ) multiple neural networks based on layers diagnosis model , back - propagation algorithm and its improved algorithm is analyzed in details
( 3 )详细分析了反向传播算法、其改进算法、基于层次分类诊断模型的多重结构神经网络。 |
| 7. | In this model , back propagation algorithm based on forward networks was conducted to learn information of historical data and to train the network weights
以人工神经网络的前馈型网络为基础结构，基于反向传播算法进行学习和训练来拟和证券价格指数的运动趋势。 |
| 8. | In order to satisfy the requirement of the given precision , the connection power of the networks is studied and adjusted using the baekpropagation training algorithm ( bp algorithm )
采用误差反向传播算法( bp算法)对网络的连接权值进行学习和调整，以满足给定的精度要求。 |
| 9. | Feedforward networks use back propagation algorithm to train a multi - layer network . after training , the multi - layer network can fit the function in the data space very well
前向网络利用反向传播算法训练多层网络，使训练后的网络较好地拟合样本空间中各点的函数值。 |
| 10. | Training state of back propagation is explained according to turbine fault sample . then improved method of using layers classification diagnosis model is put forward
并以汽轮机故障样本为例，阐述了反向传播算法的训练情况，然后提出使用层次分类诊断模型等进一步的改进方法。 |
