A comparative study of a back propagation artificial neural network and a Zerilli–Armstrong model for pure molybdenum during hot deformation

2007 ◽  
Vol 25 (5-6) ◽  
pp. 411-416 ◽  
Author(s):  
Cheng Chen ◽  
Haiqing Yin ◽  
Islam S. Humail ◽  
Yuhui Wang ◽  
Xuanhui Qu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comparative Study ◽  
Hot Deformation ◽  
Back Propagation ◽  
Pure Molybdenum ◽  
Artificial Neural

scholarly journals Comparative Study of Artificial Neural Network Based Channel Equalization Methods for MmWave Communications

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Diego Fernando Carrera ◽  
Cesar Vargas-Rosales ◽  
Noe M. Yungaicela-Naula ◽  
Leyre Azpilicueta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comparative Study ◽  
Channel Equalization ◽  
Artificial Neural

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

2017 ◽  
Vol 14 (9) ◽  
pp. 095601 ◽  
Author(s):  
Huimin Sun ◽  
Yaoyong Meng ◽  
Pingli Zhang ◽  
Yajing Li ◽  
Nan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Non Invasive ◽  
Artificial Neural
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