Karcı fractional artificial neural networks (KarcıFANN): anew artificial neural networks model without learning rate and its problems
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Tubitak Scientific & Technological Research Council Turkey
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The learning rate parameter used in classical artificial neural networks (ANNs) designed with stochastic gradient descent causes problems such as failure to learn, getting stuck in local minima, memorization, and long training times (divergence problem). To address these issues, this paper proposes a novel ANN method that uses a fractional derivative instead of Newton's derivative. This method is referred to as Karc & imath; fractional ANN (Karc & imath;FANN). In classical ANNs, the weight update is done by assigning the same constant value to the learning rate in each iteration or for a set number of iterations. In contrast, in Karc & imath;FANNs, the weight update process is carried out by calculating the fractional derivative based on the error value in each iteration. Thus, in Karc & imath;FANN, external intervention in the network is minimized compared to that of classical ANNs. Karc & imath;FANN and classical ANN methods were compared for the classification of MNIST and fashion-MNIST datasets. The Karc & imath;FANN method produces successful results for fractional derivative orders between 0.8 and 1.8. The highest accuracy values obtained in the classification of the MNIST dataset were 99.39% for Karc & imath;FANN and 99.43% for classical ANN in the training phase, and 96.76% for Karc & imath;FANN and 96.72% for classical ANN in the validation phase. The highest accuracy values obtained in the classification of the Fashion-MNIST dataset were 98.10% for Karc & imath;FANN and 98.11% for classical ANN in the training phase, and 88.56% for Karc & imath;FANN and 88.54% for classical ANN in the validation phase. The experimental studies show that Karc & imath;FANN is a competitive alternative to classical ANN. Karc & imath;FANN learns faster than classical ANN and eliminates the learning coefficient problem. Additionally, it is seen that the Karc & imath;FANN method is more successful in global modeling. However, achieving the optimal model in Karc & imath;FANN depends on the dataset and hyperparameters.
Açıklama
Anahtar Kelimeler
Artificial neural network, classification, learning rate, Karc & imath, fractional derivative, Karc & imath, fractional artificial neural network
Kaynak
Turkish Journal of Electrical Engineering and Computer Sciences
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
33
Sayı
3
