Comparison of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) Models in Simulating Polygalacturonase Production

Abstract

The artificial neural network (ANN) method was used in comparison with the adaptive neuro-fuzzy inference system (ANFIS) to describe polygalacturonase (PG) production by Bacillus subtilis in submerged fermentation. ANN was evaluated with five neurons in the input layer, one hidden layer with 7 neurons, and one neuron in the output layer. Five fermentation variables (pH, temperature, time, yeast extract concentration, and K2HPO4 concentration) served as the input of the ANN and ANFIS models, and the polygalacturonase activity was the output. Coefficient of determination (R2) and root mean square values (RMSE) were calculated as 0.978 and 0.060, respectively for the best ANFIS structure obtained in this study. The R2 and RMSE values were computed as 1.00 and 0.030, respectively for the best ANN model. The results showed that the ANN and ANFIS models performed similarly in terms of prediction accuracy.

Keywords

• Artificial intelligence
• Back-propagation network
• Polygalacturonase
• Adaptive neuro-fuzzy inference system