2) 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。
3) 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
4) 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
5) 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
6) BP neural network
BP 神经网络
1.
Application of BP Neural Network to Geotechnical Engineering;
BP 神经网络在岩土工程中的应用
2.
Then it used BP neural network to classify the feature parameters.
本文针对木材表面颜色自动分类难题,在 RGB 颜色空间提取木材图像的颜色矩作为颜色特征参数,利用 BP 神经网络对特征参数进行分类,通过输入层、输出层和隐含层的设计,传递函数的选择,确定最终网络结构。
3.
This paper applied BP neural network to classify the 10 kinds of common wood textures.
应用 BP 神经网络对常见的10种木材纹理进行了分类研究,获得了比较满意的效果。
补充资料:预警
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