EXPERIMENTAL ANALYSIS WITH SPECIFIC HEAT OF WATER-BASED ZIRCONIUM OXIDE NANOFLUID ON THE EFFECT OF TRAINING ALGORITHM ON PREDICTIVE PERFORMANCE OF ARTIFICIAL NEURAL NETWORK
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Begell House Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, the effect of the algorithms used in the training of artificial neural networks on the prediction performance of artificial neural networks has been extensively investigated. In order to make this analysis, three different artificial neural network models have been developed using the Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, which are frequently used in the literature. In the training of artificial neural networks, the specific heat values measured experimentally by the differential thermal analysis method of ZrO2/water nanofluid prepared in five different volumetric concentrations have been used. Temperature (T) and volumetric concentration (phi) are defined as input parameters of the multilayer perceptron feed-forward back-propagation artificial neural network model with 15 neurons in the hidden layer and specific heat values are predicted at the output layer. The results showed that artificial neural networks are an ideal tool for predicting the thermophysical properties of nanofluids. However, it has been found that the artificial neural network designed with the Bayesian regularization training algorithm has the highest prediction performance with an average margin of deviation of 0.00009%. It has been observed that the artificial neural network developed with the scaled conjugate gradient training algorithm has the lowest prediction performance with an average error of -0.0032%.
Açıklama
Anahtar Kelimeler
artificial neural network, error rates, training algorithm, nanofluid, specific heat
Kaynak
Heat Transfer Research
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
52
Sayı
7
