scholarly journals Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination

2019 ◽  
pp. 396-408
Author(s):  
Mohsin Abbas ◽  
Peter van Rosmalen ◽  
Marco Kalz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Backward Elimination ◽  
Artificial Neural

scholarly journals Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2570
Author(s):  
Tomasz Trzepieciński ◽  
Marcin Szpunar ◽  
Ľuboš Kaščák
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Variable Load ◽  
Training Methods ◽  
Backward Elimination ◽  
Drawing Test ◽  
Strip Drawing ◽  
Rms Error ◽  
Artificial Neural ◽  
Quasi Newton

This paper presents the application of multi-layer artificial neural networks (ANNs) and backward elimination regression for the prediction of values of the coefficient of friction (COF) of Ti-6Al-4V titanium alloy sheets. The results of the strip drawing test were used as data for the training networks. The strip drawing test was carried out under conditions of variable load and variable friction. Selected types of synthetic oils and environmentally friendly bio-degradable lubricants were used in the tests. ANN models were conducted for different network architectures and training methods: the quasi-Newton, Levenberg-Marquardt and back propagation. The values of root mean square (RMS) error and determination coefficient were adopted as evaluation criteria for ANNs. The minimum value of the RMS error for the training set (RMS = 0.0982) and the validation set (RMS = 0.1493) with the highest value of correlation coefficient (R2 = 0.91) was observed for a multi-layer network with eight neurons in the hidden layer trained using the quasi-Newton algorithm. As a result of the non-linear relationship between clamping and friction force, the value of the COF decreased with increasing load. The regression model F-value of 22.13 implies that the model with R2 = 0.6975 is significant. There is only a 0.01% chance that an F-value this large could occur due to noise.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document