GERİ-YAYILMALI ÖĞRENME ALGORİTMASINDAKİ ÖĞRENME PARAMETRELERİNİN GENETİK ALGORİTMA İLE BELİRLENMESİ

Abstract

Bu çalışmada, ileri beslemeli bir sinir ağının eğitiminde kullanılan geri-yayılmalı öğrenme algoritmasındaki öğrenme parametreleri genetik algoritmalar kullanılarak belirlenmiştir. Öğrenme parametreleri öğrenme ve momentum katsayıları olarak bilinmektedir. Öğrenme parametreleri ağın öğrenme hızının arttırılması, öğrenme esnasında oluşabilecek osilasyonların giderilmesi ve lokal minimumlardan kaçılması gibi özellikleri belirlemektedirler. Dolayısıyla bu parametrelerin uygun biçimde seçilmesi ağın daha etkin olarak eğitilmesinde oldukça önemlidir. Öğrenme parametrelerinin genetik algoritma ile belirlenmesi için, dört katmanlı ileri beslemeli bir ağ tasarlanmıştır. Tasarlanan ağdaki üç öğrenme ve üç momentum katsayısı, genetik bir kromozom ile ifade edilmiştir. Çalışmanın amacı; en uygun kromozomun seçilmesidir. Ortaya konulan yöntemin test edilmesinde özel tanımlı iki boyutlu regresyon problemlerinden yararlanılmıştır. Yapılan test çalışması ortaya konulan yöntemin geleneksel sabit parametreli öğrenme algoritmasına göre daha etkin olduğunu göstermiştir.

Keywords

• Geri-yayılmalı öğrenme algoritması, öğrenme parametreleri, ileri beslemeli sinir ağları,genetik algoritmalar, öğrenme katsayısı, momentum katsayısı

DETERMINATION OF THE LEARNING PARAMETERS IN BACKPROPAGATION LEARNING ALGORITHM BY GENETIC ALGORITHM

Abstract

In this study, the learning parameters in backpropagation learning algorithm is determined by using genetic algorithm, which is used for training of a feedforward neural network. Learning parameters are known as learning rate and momentum rate. The learning parameters find out features such as acceleration of learning, dampening oscillations and getting rid of local minima during learning of the network. Therefore, a selection of these parameters is quite important for training of the network more efficiently. A feedforward neural network with four layers is designed to define learning parameters by genetic algorithm. Three learning rates and three momentum coefficients in the designed network have been denoted with one genetic chromosome. The aim of the study is to choice fittest chromosome. In order to test the proposed method, a specific described two dimensional regression problems are utilized. Test results show that the suggested method is more efficient than conventional learning algorithm with fixed parameter.

Keywords

• Backpropagation learning algorithm, learning parameters, feedforward neural networks, genetic algorithms, learning rate, momentum rate

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