scholarly journals Correction to: Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach

2020 ◽  
Vol 19 (5) ◽  
pp. 331-331
Author(s):  
Anwar Hasan Abdullah Othman ◽  
Salina Kassim ◽  
Romzie Bin Rosman ◽  
Nur Harena Binti Redzuan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Network Approach ◽  
Accuracy Improvement ◽  
Neural Network Approach ◽  
Market Prices ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

scholarly journals Cavitation Damage Prediction of Stainless Steels Using an Artificial Neural Network Approach

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 506 ◽  
Author(s):  
Guiyan Gao ◽  
Zheng Zhang ◽  
Cheng Cai ◽  
Jianglong Zhang ◽  
Baohua Nie
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steels ◽  
Prediction Accuracy ◽  
Network Approach ◽  
Neural Network Approach ◽  
Cavitation Damage ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Artificial Neural Network Approach

Cavitation damage has not been well predicted because of its complex relationship of many mechanical and microstructural factors. An artificial neural network approach of the back-propagation network was used to predict cavitation damage of stainless steels, 316L and 420, in terms of the significant influence of cavitation time, roughness, and residual stress on cavitation damage. Mean depth of erosion was used to quantitatively describe cavitation damage of 316L and 420. Prediction accuracy was improved by analyzing the effects of the number and type of input nodes, the number of nodes in the hidden layer, and different activation functions on prediction accuracy. The best performance was in the model with the input nodes of cavitation time and roughness, eleven nodes in the hidden layer, and the activation function of logsig.

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