The Effect of Using Fractional Derivatives in ANNs on Learning

Year 2025, Volume: 10 Issue: 2, 153 - 179, 01.12.2025

Meral Karakurt

Abstract

During the design phase of artificial neural networks (ANNs), optimization techniques, the number of layers and neurons, as well as activation and loss functions—collectively referred to as hyperparameters—play a decisive role in model performance. Gradient-based optimization methods are widely employed throughout the learning process of these models. However, Stochastic Gradient Descent (SGD) often encounters critical issues such as overfitting, underfitting, poor generalization, and failure to converge. To address these challenges and to achieve more effective modeling of systems through fractional-order derivatives, the KarcıFANN optimization method is employed.
In this study, models employing the KarcıFANN and SGD learning algorithms were designed for the classification of the MNIST and Dry Bean datasets. These models were trained using various configurations of layer numbers, as well as different activation and loss functions, and their performances were comparatively evaluated. The results demonstrate that incorporating fractional-order derivatives leads to improved model performance, highlighting the potential of the KarcıFANN method as an effective alternative to the conventional SGD algorithm.

Keywords

KarcıFANN , SGD , Fractional Derivatives , Classification.

YSA’larda Kesir Dereceli Türev Kullanımının Öğrenmeye Etkisi

Abstract

YSA’ların tasarımı aşamasında kullanılan optimizasyon yöntemleri, katman ve nöron sayıları, aktivasyon ve hata fonksiyonları gibi hiperparametreler, modellerin başarımını belirlemektedir. Gradyan tabanlı optimizasyon yöntemleri, modellerin öğrenme sürecinde yaygın olarak kullanılmaktadır. SGD yönteminde ezberleme, öğrenememe, genelleme yapamama, yakınsayamama gibi önemli problemlerle karşılaşılmaktadır. Bu problemleri çözmek ve kesir dereceli türevlerle sistemleri daha iyi modellemek amacıyla KarcıFANN optimizasyon yöntemi kullanılmaktadır.
Bu çalışmada, MNIST ve Dry Bean veri setlerinin sınıflandırılması amacıyla KarcıFANN ve SGD öğrenme yöntemlerinin kullanıldığı modeller tasarlanmıştır. Bu modeller, çeşitli katman sayısı konfigürasyonları ile farklı aktivasyon ve hata fonksiyonları kullanılarak eğitilmiş ve performansları karşılaştırmalı olarak değerlendirilmiştir. Elde edilen bulgular, kesir dereceli türev kullanımıyla modellerin performansının arttığını ve KarcıFANN yönteminin SGD’ye alternatif bir yaklaşım olarak değerlendirilebileceğini göstermektedir.

Keywords

KarcıFANN , SGD , Kesir Dereceli Türev , sınıflandırma.

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