Optimizing feed-forward neural networks using cascaded genetic algorithm

2003 ◽  
Author(s):  
Lin-xia Zhou ◽  
Ming Li ◽  
Xiaoqin Yang
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Feed Forward ◽  
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.

An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals

1997 ◽  
Vol 11 (05) ◽  
pp. 717-734 ◽  
Author(s):  
J. M. Westall ◽  
M. S. Narasimha
Keyword(s):  
Neural Networks ◽  
Production System ◽  
Background Noise ◽  
Evolutionary Approach ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Decision Functions

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

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