Fonksiyon yaklaşımı probleminde esnek küçük-dünya ağlarının topolojik değişiminin performansa etkisi

Year 2020, Volume: 7 Issue: 3, 1398 - 1406, 30.09.2020

Okan Erkaymaz

https://doi.org/10.31202/ecjse.787819

Abstract

İleri yönlü yapay sinir ağları performansı topolojiyle doğrudan ilişkilidir. Son yıllarda, topolojik değişikliklerle ağ performansının artırılması üzerine yapılan çalışmalar çok önemli bir süreç haline gelmiştir. Bu nedenle, nöronlar arası sinaptik bağlantıların değiştirildiği bir yaklaşım olan küçük-dünya ağ modeli gelecek çalışmalar için önemli bir yere sahiptir. Bu çalışmada, fonksiyon yaklaşımı problemleri için esnek küçük-dünya ağ modelinin katmansal ve nöronal değişimlerle performansının nasıl değiştiği araştırılmıştır. Elde edilen sonuçlar gösterdi ki, geleneksel esnek yapay sinir ağı topolojisine göre esnek küçük-dünya ağının performansı çok daha fazla iyileştirdiği ve katman sayısının artırılmasının ağ performansına ciddi katkı sağladığı gözlemlenmiştir. Diğer taraftan, katman sayısı ile birlikte gizli katman nöron sayısının artırıldıkça performansa negatif etki ettiği görülmüştür. Bu bağlamda, çoklu gizli katmana sahip esnek küçük-dünya ağlarının fonksiyon yaklaşımı probleminin çözümünde pozitif etki ettiği gösterilmiş ve özellikle bu modelin gelecekte derin nöron ağları performansının geliştirilmesinde anahtar rol oynayacağı vurgulanmıştır.

Keywords

Newman-Watts algoritması, esnek küçük-dünya ağları, fonksiyon yaklaşımı problemi, bağlantı yenileme

Effect of topological changes of resilient small-world networks on performance in the function approach problem

Abstract

Performance of Feed forward neural networks is directly related to topology. In recent years, studies on improving performance of network with topological changes have become a very important process. Therefore, the small-world network model, an approach in which synaptic connections between neurons are altered, has an important place for future studies. In this study, how the performance of the resilient small-world network model changes with layered and neuronal revisions has been investigated for function approach problems. The results show that the resilient small-world network improves the performance much more than the traditional resilient artificial neural network topology and it has been observed that increasing the number of layers significantly contributes to the network performance. On the other hand, It has been observed that when the number of hidden layer neurons is increased with the number of layers, the performance has a negative effect. In this context, it has been shown that resilient small-world networks with multiple hidden layers have a positive effect on the solution of the function approach problem and also it is emphasized that this model will play a key role in improving the performance of deep neuron networks in the future.

Keywords

Newman-Watts algorithm, resilient small-world networks, function approach problem, rewiring

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