Radyal Tabanlı Yapay Sinir Ağlarının Kurutma Kinetiğinin Modellenmesinde Kullanımı    
Yazarlar (3)
Adil Koray Yıldız
Yozgat Bozok Üniversitesi, Türkiye
Doç. Dr. Muhammed TAŞOVA Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Doç. Dr. Hakan POLATCI Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Makale Türü Özgün Makale
Makale Alt Türü Ulusal alan endekslerinde (TR Dizin, ULAKBİM) yayınlanan tam makale
Dergi Adı Türk Tarım - Gıda Bilim ve Teknoloji Dergisi
Dergi ISSN 2148-127X
Dergi Tarandığı Indeksler TR DİZİN
Makale Dili Türkçe
Basım Tarihi 02-2020
Cilt No 8
Sayı 2
Sayfalar 511 / 514
DOI Numarası 10.24925/turjaf.v8i2.511-514.3196
Makale Linki http://dx.doi.org/10.24925/turjaf.v8i2.511-514.3196
Özet
Drying method is preferred in agricultural products since it provides advantages in many processes such as increasing the strength of products, transporting and storing. It is necessary to estimate the drying behavior of the products in order to achieve the best drying without reducing the product quality. For this reason, many numerical drying models have been developed to estimate the drying kinetics of the products. Recently, artificial neural networks have been widely used for the development of these models. Artificial neural networks are mathematical models that work in a similar way to natural neuron cells. Radial based artificial neural networks are radial based activation functions in the transition to the hidden layer unlike other networks. In this study, modeling of drying kinetics with radial based networks was investigated. For the experiment, red hot pepper (Capsicum annuum L.) was dipped in boiled water and microwave pretreatments and, then dried in the oven at 65ÂC. The absorbable moisture values were calculated during the drying period. The radial based artificial neural network models were trained with the drying time values as input and the absorbable moisture values as output. The study was carried out with two data sets including all data and only the average. In trainings with all data, R value of the best model was calculated as 0.9566. R was calculated as 0.9998 with average data.
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