Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

2017 ◽  
Vol 120 ◽  
pp. 568-580 ◽  
Author(s):  
Bo Xu ◽  
Han-Cheng Dan ◽  
Liang Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Asphalt Pavement ◽  
Temperature Prediction ◽  
Cold Regions

Temperature Prediction Model in Transition Ladle based on Guided Mutation GA-BP Neural Network

2021 ◽  
Author(s):  
Gaopeng Han ◽  
Jianyan Tian ◽  
Zhien Li ◽  
Yongfeng Lv ◽  
Bo Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Temperature Prediction ◽  
Guided Mutation

scholarly journals Temperature prediction model for solar greenhouse based on improved BP neural network

2020 ◽  
Vol 1639 ◽  
pp. 012036
Author(s):  
Xingjian Li ◽  
Xiangnan Zhang ◽  
Yawei Wang ◽  
KaifengZhang ◽  
Yi-fei Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Temperature Prediction ◽  
Solar Greenhouse

A new prediction model of CO2 diffusion coefficient in crude oil under reservoir conditions based on BP neural network

Energy ◽  
2021 ◽  
pp. 122286
Author(s):  
Hao Chen ◽  
Yu Wang ◽  
Mingsheng Zuo ◽  
Chao Zhang ◽  
Ninghong Jia ◽  
...  
Keyword(s):  
Neural Network ◽  
Diffusion Coefficient ◽  
Prediction Model ◽  
Crude Oil ◽  
Bp Neural Network ◽  
Co2 Diffusion ◽  
Reservoir Conditions

Research on Stamping Spring-Back Prediction for Car Body Panel Based on BP Neural Network

2010 ◽  
Vol 97-101 ◽  
pp. 250-254 ◽  
Author(s):  
Xin Jian Zhou
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Front Panel ◽  
Spring Back ◽  
Test Analysis ◽  
Car Body ◽  
Neural Network Prediction ◽  
Mould Design ◽  
Bp Neural Network Model

On the basis of orthogonal test analysis of variance, BP neural network is used to forecast quantitatively the stamping spring-back of front panel of a car body, namely the engine hood, under the conditions of different stamping parameters. Firstly, BP neural network prediction model is established and sample training is done in Matlab. Then, the spring-back prediction using BP neural network and the result of spring-back simulation using Dynaform is compared to verify the precision and stability of the prediction model. Lastly, modification is made to the BP neural network according to practical stamping parameters and an efficient BP neural network model is established. Using this model, stamping spring-back prediction for the front panel of a car body is made. The spring-back prediction could then be used for spring-back compensation in the mould design of the front panel.

Prediction of Sand Thickness Using Seismic Attributes Based on BP-Neural Network

2012 ◽  
Vol 524-527 ◽  
pp. 180-183
Author(s):  
Feng Gao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Seismic Attributes ◽  
Bp Network ◽  
Sand Body ◽  
Precise Prediction ◽  
Energy Maximum ◽  
Bp Model ◽  
Rms Amplitude

Total energy, maximum peak amplitude and RMS amplitude are sensitive to sand body, and they are non-linear relations with sand thickness. In this study, a three-layer BP neural network is employed to build the prediction model. Nine samples were analyzed by three-layer BP network. The relationships were produced by BP network between sand thickness and the three seismic attributes. The precise prediction results indicate that the three-layer BP network based modeling is a practically very useful tool in prediction sand thickness. The BP model provided better accuracy in prediction than other methods.

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