Genetik Algoritma Kullanılarak İleri Beslemeli Bir Sinir Ağında Etkinlik Fonksiyonlarının Belirlenmesi

Bu çalışmada çok katmanlı ileri besleneli bir sinir ağının tüm katmanlarındaki üyelik fonksiyonları genetik algoritma kullanarak belirlenmiştir. Bir sinir ağının etkinliğini gösteren temel ölçüt aynı sayıda düğüm ve bağlantı ağırlığı ile istenen sonuca daha iyi yaklaşabilmektir. Bu performansı belirleyen en önemli parametrelerden birisi uygun etkinlik fonksiyonlarının seçilmesidir. Klasik sinir ağı tasarımında genellikle bilinen etkinlik fonksiyonlarından birisi seçilerek ağ tasarımı gerçekleştirilmektedir. Bu çalışmada etkinlik fonksiyonları için bir tablo oluşturulmuş ve her bir düğüm için en uygun etkinlik fonksiyonu genetik algoritma ile bu tablodan seçilmiştir. Klasik sabit yapılı bir sinir ağı ile önerilen genetik tabanlı sinir ağının performansının karşılaştırılması için 2 boyutlu regresyon problem kümesi kullanılmıştır. Test sonuçları ortaya konulan yöntemin oldukça yüksek bir yaklaşım kapasitesine sahip olduğunu göstermiştir.

Anahtar Kelimeler:

İleri beslemeli sinir ağları, Genetik algoritmalar, Etkinlik fonksiyonları.

Determination of Activation Functions in A Feedforward Neural Network by using Genetic Algorithm

In this study, activation functions of all layers of the multilayered feedforward neural network have been determined by using genetic algorithm. The main criteria that show the efficiency of the neural network is to approximate to the desired output with the same number nodes and connection weights. One of the important parameter to determine this performance is to choose a proper activation function. In the classical neural network designing, a network is designed by choosing one of the generally known activation function. In the presented study, a table has been generated for the activation functions. The ideal activation function for each node has been chosen from this table by using the genetic algorithm. Two dimensional regression problem clusters has been used to compare the performance of the classical static neural network and the genetic algorithm based neural network. Test results reveal that the proposed method has a high level approximation capacity.

Keywords:

Feedforward neural networks, Genetic algorithms, Activation functions.,

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