Periyodik Sınır Değer Problemlerinin Artık Sinir Ağı Çözümleri için Optimizasyon Yöntemleri Üzerine Karşılaştırmalı Bir Çalışma

Yıl 2024, Cilt: 1 Sayı: 1, 30 - 39, 06.07.2024

Gülsüm İşman , Korhan Günel

Öz

Bu makale, periyodik sınır koşullarına sahip ikinci mertebeden diferansiyel denklemlerin çözümünde artık sinir ağlarının kullanımını araştırmaktadır. Çalışmada, adi diferansiyel denklemler için mevcut nümerik tekniklerden bahsedildikten sonra, verilen denklemlerin artık sinir ağları (ResNet) ile yaklaşık çözümleri elde edilmiştir. Bu süreçte oluşturulan artık sinir modelinin eğitimi aşamasında, derin öğrenmede sıklıkla kullanılan ve türev tabanlı optimizasyon algoritmalarından olan AdaDelta, Adam, Nesterov Momentum ve Gradient Descent gibi yöntemleri kullanarak çözüme olan etkilerini incelemek için toplam karesel hata (SSE) metriklerine odaklanan deneysel çalışmalar yapılmıştır. Bulgularımız, önerilen derin öğrenme modelinin nümerik analizde kullanılan geleneksel sayısal çözüm yöntemlerine karşı etkili bir alternatif olduğunu ortaya koymaktadır.

Anahtar Kelimeler

Yapay Sinir Ağları, Derin Öğrenme, Diferansiyel Denklemler, Optimizasyon, Periyodik Sınır Koşulları

Etik Beyan

Bu makale hayvan ve/veya insan deneyleri içermez. Etik kurul onayı gerektirmemektedir.

A Comparative Study on Optimization Methods for Residual Neural Network Solutions of Periodic Boundary Value Problems

Öz

This paper investigates using residual neural networks to solve second-order differential equations with periodic boundary conditions. After mentioning the existing numerical methods for ordinary differential equations, approximate solutions of the given equations are obtained with residual neural networks (ResNet). In the training stage of the residual neural model constructed in this process, experimental studies focusing on the Sum of Squared Error (SSE) metrics were conducted to examine their effects on the solution using methods such as AdaDelta, Adam, Nesterov Momentum, and Gradient Descent, which are frequently used in deep learning and derivative-based optimization algorithms. Our findings show that the proposed deep learning model is an effective alternative to traditional numerical solution methods used in numerical analysis.

Anahtar Kelimeler

Artificial Neural Network, Deep Learning, Differential Equations, Optimization, Periodic Boundary Conditions

Etik Beyan

This article does not involve animal or human experiments, and no ethical approval is necessary.

Kaynakça

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