Developing Optimal Feed-Forward Neural Networks using a Constructive Dynamic Training Method and Pruning with a Genetic Algorithm

2009 ◽  
Author(s):  
W. Wang ◽  
W. Lu ◽  
X. Wang ◽  
A.Y.T. Leung
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Training Method ◽  
Feed Forward ◽  
Optimal Feed ◽  
Feed Forward Neural Networks

scholarly journals Role of Feed Forward Neural Networks Coupled with Genetic Algorithm in Capitalizing of Intracellular Alpha-Galactosidase Production byAcinetobactersp.

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Sirisha Edupuganti ◽  
Ravichandra Potumarthi ◽  
Thadikamala Sathish ◽  
Lakshmi Narasu Mangamoori
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Squared Error ◽  
Feed Forward Neural Networks ◽  
Alpha Galactosidase

Alpha-galactosidase production in submerged fermentation byAcinetobactersp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks was further analysed by using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), andR2-value for training and testing data. Using hybrid neural networks and genetic algorithm, alpha-galactosidase production was improved from 7.5 U/mL to 10.2 U/mL.

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