İleri Beslemeli Yapay Sinir Ağının Eğitiminde Meta-Sezgisel Yaklaşımlar

Öz

Sistem kimliklendirme ve modelleme için en yaygın kullanılan yapay zekâ tekniklerinden biri yapay sinir ağlarıdır. Yapay sinir ağları ile etkili sonuçlar elde etmek için etkili bir eğitim sürecine ihtiyaç duyulmaktadır. Meta-sezgisel algoritmalar pek çok gerçek dünya probleminin çözümünde başarılı bir şekilde kullanılmaktadır. Özellikle yapay sinir ağı eğitiminde, ağa ait parametrelerin optimizasyonu gerekmektedir. Son zamanlarda, bu amaçla meta-sezgisel algoritmalar kullanılmakta ve başarılı sonuçlar elde edilmektedir. Literatürde pek çok meta-sezgisel algoritma bulunmaktadır. Meta-sezgisel algoritmaların performansları problem türüne göre farklılık göstermektedir. Bu çalışma kapsamında ileri beslemeli yapay sinir ağının eğitiminde, yapay arı koloni algoritması, parçacık sürü algoritması, armoni arama, arı algoritması, çiçek tozlaşma algoritması ve guguk kuşu arama gibi popüler meta-sezgisel algoritmaların performansları değerlendirilmiştir. Uygulamalar için XOR, 2-bit parity ve 3-bit parity problemleri kullanılmıştır. Tüm problemler için elde edilen sonuçlar çözüm kalitesi ve yakınsama hızı açısından değerlendirilmiştir. Genel olarak ilgili problemlerin çözümü için meta-sezgisel algoritma tabanlı ileri yapay sinir ağı eğitiminin başarılı olduğu gözlemlenmiştir. En iyi sonuçlar ise yapay arı koloni algoritması ve guguk kuşu arama ile bulunmuştur.

Anahtar Kelimeler

• İleri beslemeli yapay sinir ağı
• Meta-sezgisel algoritmalar
• Yapay sinir ağı eğitimi
• Modelleme
• Optimizasyon

The Meta-Heuristics Approaches in Training Feed-Forward Neural Networks

Öz

Artificial neural network is one of the most widely used artificial intelligence techniques for system identification and modeling. An effective training process is needed to obtain effective results with artificial neural networks. Metaheuristic algorithms have been used successfully in solving many real-world problems. Especially, optimization of the parameters of the network is required in artificial neural network training. Recently, metaheuristic algorithms have been used for this purpose and successful results have been obtained. There are many metaheuristic algorithms in the literature. The performances of meta-heuristic algorithms can differ according to the problem type. In this study, the performances of popular metaheuristic algorithms such as artificial bee colony algorithm, particle swarm optimization, harmony search, bee algorithm, flower pollination algorithm and cuckoo search are evaluated in the training of feed forward neural network. XOR, 2-bit parity and 3-bit parity problems are utilized for applications. The results obtained for all problems are evaluated in terms of solution quality and convergence speed. In general, it has been observed that neural network training based on metaheuristic algorithm is successful for solving related problems. The best results are found by using artificial bee colony algorithm and cuckoo search.

Anahtar Kelimeler

• Feed forward neural network
• Metaheuristic algorithms
• Neural network training
• Modeling
• Optimization

Kaynakça

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