A Novel Deep Learning Algorithm: Hybrid Layer Collaboration-based Forward-Forward Network (HiLaCob-FF)
EMO Bilimsel Dergi, cilt.16, sa.2, ss.48-63, 2026 (TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 2
- Basım Tarihi: 2026
- Dergi Adı: EMO Bilimsel Dergi
- Derginin Tarandığı İndeksler: TR DİZİN (ULAKBİM)
- Sayfa Sayıları: ss.48-63
- Van Yüzüncü Yıl Üniversitesi Adresli: Evet
Özet
This study proposes a new learning algorithm, the "Hybrid Layer Collaboration-based Forward-Forward Network (HiLaCob-FF)," which is based on the forward-forward algorithm. The HiLaCob-FF algorithm employs a new loss function, a global loss rather than a local loss, and hyperparameter optimization. It applied to MNIST, Fashion MNIST, and CIFAR-10 datasets, demonstrating its generalization ability in recognizing patterns and making inferences. This study underscores the importance of alternative approaches in deep learning. It is concluded that the proposed new loss function improved the model training losses. As a result, with the latest loss function applied to the MNIST dataset, the model's training loss decreased by 1.42% to 0.87%. Similarly, the error rate reduced from 40.91% to 40.36% on the CIFAR-10 dataset. Our proposed HiLaCob-FF model achieves 92.32% success on the MINIST dataset, 78.29% on the FashionMINIST dataset, and 74.20% on the CIFAR-10 dataset. Although these performances are not as high as those of backpropagation-based models, the results are essential for developing forward-forward networks