A Constructive Neural Network Learning Method Based on Quotient Space and Its Application in Coal Mine Gas Prediction

2010 ◽  
Author(s):  
Yujun Liu ◽  
Yueqin Zhang ◽  
Yu Zhu ◽  
Zhenxing Zhao
Keyword(s):  
Neural Network ◽  
Coal Mine ◽  
Quotient Space ◽  
Learning Method ◽  
Neural Network Learning ◽  
Network Learning ◽  
Coal Mine Gas ◽  
Mine Gas

scholarly journals Development of interatomic potential for Al–Tb alloys using a deep neural network learning method

2020 ◽  
Vol 22 (33) ◽  
pp. 18467-18479 ◽  
Author(s):  
L. Tang ◽  
Z. J. Yang ◽  
T. Q. Wen ◽  
K. M. Ho ◽  
M. J. Kramer ◽  
...  
Keyword(s):  
Neural Network ◽  
Ab Initio ◽  
Structural Properties ◽  
Deep Neural Network ◽  
Interatomic Potential ◽  
Learning Method ◽  
Neural Network Learning ◽  
Network Learning ◽  
Formation Energies

The developed deep neural network (DNN) potential can describe the structural properties of the Al90Tb10 liquid and the formation energies of Al–Tb crystals with the accuracy of ab initio calculations.

Coal Mine Gas Prediction Model Based on Ant Colony Neural Network

2012 ◽  
Vol 546-547 ◽  
pp. 3-7 ◽  
Author(s):  
Jia Tang Cheng ◽  
Hui Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Coal Mine ◽  
Prediction Accuracy ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Gas Emission ◽  
Bp Network ◽  
Coal Mine Gas ◽  
Mine Gas

In order to improve the prediction accuracy and prediction speed of coal mine gas emission, ant colony algorithm combining with neural network is used for prediction models design. Choose an important factor influencing gas emission, establish of its neural network prediction model. Select the network mean square error as the objective function, through the ant colony algorithm iteration achieve optimal BP network weights, and use the optimized BP network for gas emission prediction. Simulation results show that the method has high fitting prediction accuracy.

Sign in / Sign up

Export Citation Format

Share Document