Fındık Kabuğu Kurutma Sürecinde Yapay Sinir Ağları ve Bulanık Mantık Yöntemlerinin Karşılaştırılması

Year 2023, Volume: 6 Issue: 2, 50 - 55, 31.12.2023

Mert Levent Halil Nusret Buluş Soner Çelen Aytaç Moralar

https://doi.org/10.55581/ejeas.1388492

Abstract

Gıda ürünlerinin kurutularak saklanması kullanım ömrünü uzatması açısından uzun yıllardır kullanılan bir tekniktir. Gıdaların kurutularak saklanması gibi gıda artıklarının da kurutulması son yıllarda çalışmalara konu olmuştur. Ürün kurutma işlemi, ürün içerisindeki nem miktarının azaltılması anlamına gelmektedir. Nem miktarının azaltılması amacıyla kurulmuş olan düzenekler maliyetli ve daha çok kuruma oranının tespitinin tecrübeye dayandığı sistemlerdir. Bu sebeple son yıllarda kurutma süreçlerinin matematiksel olarak modellenmesi, yapay zeka yöntemleri ile sistem davranışının bir modelinin oluşturulması çalışmaları ilgi çekmiştir. Bu çalışmada fındık kabuğu kurutma işleminin yapay zeka tekniklerinden olan yapay sinir ağları ve bulanık mantık yöntemleri ile modellenmesi gerçekleştirilmesi hedeflenmiş ve oluşturulan modellerin kuruma oranının deneysel sonuçlarına yakınlığı incelenmiştir.

Keywords

ANFIS, Bulanık Mantık, Kurutma, Levenberg Marquardt, Tahmin, Yapay Sinir Ağları

Comparison of Artificial Neural Networks and Fuzzy Logic Methods in the Hazelnut Shell Drying Process

Abstract

Drying and storing food products have been widely used techniques for extending shelf life for many years. In recent times, there has been a focus on the drying of food residues, similar to the preservation of foods by drying. The process of drying a product involves reducing the moisture content within the product. However, the devices established to reduce moisture content are often costly and rely heavily on experience-based systems for determining the drying ratio. Therefore, in recent years, there has been significant interest in the mathematical modeling of drying processes and the creation of a model for system behavior using artificial intelligence methods. This study aims to model the drying process of hazelnut shells using artificial intelligence techniques, specifically artificial neural networks and fuzzy logic methods. The proximity of the models created to the experimental results of the drying ratio is examined.

Keywords

ANFIS, Artifical Neural Network, Drying, Fuzzy Logic, Levenberg Marquardt, Prediction

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