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1)  quick BP neural network
快速BP神经网络
2)  quick BP network
快速BP网络
1.
To optimize the structure of neural network, this paper improves the quick BP network by self configuration method, which overcomes the restrictions of selecting the number of suitable hidden nodes only through experiments in the past and makes it more reasonable to select the number of hidden nodes of neural network.
从优化网络结构角度出发 ,对快速BP网络用自构方法进行了改进 ,克服了以往只能依靠实验结果选择合适隐节点个数的局限性 ,使神经网络隐节点个数的选取更加合理 。
3)  back propagation neural network
BP神经网络
1.
Analysis of lateral deformation of deep excavation based on back propagation neural network and fuzzy logical control;
基于BP神经网络与模糊控制的深基坑开挖侧向变形分析
2.
Reliability analysis of distributed sensor network based on back propagation neural network;
基于BP神经网络的分布式传感器网络的可靠性分析
3.
According to its features a short-term load forecasting model is built in which the autoregressive integrated moving average (ARIMA) is integrated with back propagation neural network (BPNN).
该模型利用ARIMA方法对线性时间序列逼近能力强的特点首先对预测日负荷进行预测,然后应用BP神经网络方法对预测结果进行修正,因此克服了单一算法存在的不足。
4)  BP neural network
BP神经网络
1.
Gas content prediction based on BP neural network;
基于BP神经网络的瓦斯含量预测
2.
Study of screw pump well fault diagnosis based on BP neural network;
基于BP神经网络的螺杆泵井故障诊断方法
3.
Application of Particle Swarm Optimization based BP neural networks to atmosphere environment assessment of thermal power plants;
基于粒子群优化的BP神经网络在火电厂大气环境评价中的应用
5)  BP network
BP神经网络
1.
The Evaluation of Formability Grade of Fabrics Based on Bp network;
Bp神经网络识别服装面料成形性探讨
2.
The application of BP Network to the support of underground tunnel;
BP神经网络在地下巷道支护中的应用
3.
The application study of BP network in mechanical optimization design;
机械优化设计中BP神经网络的应用研究
6)  BP artificial neural network
BP神经网络
1.
Rock mass quality assessment based on BP artificial neural network (ANN)——a case study of borehole BS03 in Jiujing segment of Beishan,Gansu;
基于BP神经网络的岩体质量评价——以甘肃北山旧井地段BS03号钻孔为例
2.
Calculation and Prediction of Phase Equilibrium for Natural Gas Hydrate Based on BP Artificial Neural Network;
基于BP神经网络的天然气水合物相平衡计算及预测
3.
Water Quality Evaluation of Qingyijiang River with BP Artificial Neural Network;
BP神经网络在青弋江水质评价上的应用
补充资料:神经网络BP算法
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性质:它是D.Rumellart等人提出的一个监督训练多声能神经网络的算法,每一个训练范例在网络中经过两遍传递计算:一遍向前传播计算,从输入层开始,传递各层并经过处理后,产生一个输出,并得到一个该实际输出和所所需输出之差的差错矢量;一遍向反向传播计算,从输出层至输入层,利用差错矢量对权值进行逐层修改。BP算法有很强的数学基础,戏剧性地扩展了神经网络的使用范围,产生了许多应用成功的实例,对神经网络研究的再次兴起过很大作用。

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