Comparison of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in simulating polygalacturonase production
Yükleniyor...
Dosyalar
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
North Carolina State Univ Dept Wood & Paper Sci
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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 (R-2) 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 R-2 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.
Açıklama
Anahtar Kelimeler
Back-Propagation Network, Artificial Intelligence, Polygalacturonase, Adaptive Neuro-Fuzzy Inference System
Kaynak
Bioresources
WoS Q Değeri
Q2
Scopus Q Değeri
Cilt
11
Sayı
4
