Karcı fractional order neural network (KarcıFANN): solving learning rate, overfitting and underfitting problems
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Gazi Univ, Fac Engineering Architecture
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The performance of artificial neural networks (ANN) is affected by the selection of hyperparameters. Learning coefficient is a hyperparameter that significantly affects this performance. Choosing the right learning coefficient to achieve optimum success with different models and datasets is a difficult and time-consuming process. Inappropriate learning coefficient can cause problems such as network failure to learn, memorization, gradient explosion and loss. In the Karc & imath; Fractional Neural Network (Karc & imath;FANN) method proposed in this article, the weight update process is performed by using the fractional derivative instead of the learning coefficient, which is a fixed number in Classical ANNs where the Stochastic Gradient Descent (SGD) method is used. Thus, in the Karc & imath;FANN method, a fractional derivative that changes according to the error value obtained in each iteration will be used and thus external intervention to the network will be minimized, thus contributing to the literature. In the study, the results of the Classical ANN and Karc & imath;FANN methods with the same initial and parameter values were compared in order to classify the Kuzushiji_MNIST, GinaPrior2 and SignMnist data sets. In the experimental studies conducted by giving values between 0.1-5.0 to the alpha parameter, which is the fractional order of the fractional derivative, and to the learning coefficient in Classical ANN, it was observed that the Karc & imath;FANN method performed better than the Classical ANN in the classification of Kuzushiji-Mnist and GinaPrior2 data sets, especially between 3.0-5.0. It was observed that the problems of memorization and learning that were encountered in Classical ANN were eliminated in the Karc & imath;FANN method. In addition, the generalizability of the Karc & imath;FANN method was experienced by running it on multiple data sets.
Açıklama
Anahtar Kelimeler
Artificial neural networks, stochastic gradient descent, learning rate, karc & imath, fractional artificial neural networks, fractional order derivative
Kaynak
Journal of the Faculty of Engineering and Architecture of Gazi University
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
40
Sayı
4
