Dergiler / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 32 Sayı: 4
İleti dizi birim hücre tasarımı için karınca aslanı optimizasyonu ile geliştirilmiş çok katmanlı algılayıcı modeli eğitimi
- Sayfa
- 685–695
- Yayın Tarihi
- 2026-07-13
Özet
Bu çalışmada, öncelikle bir ileti dizi anten birim hücresi tasarlanmış ve tasarım parametreleri ele alınarak iletim fazı karakteristiği elde edilmiştir. Çalışmanın ikinci adımında ise, standart Çok Katmanlı Algılayıcılar (Multi-Layer Perceptron, MLP), ve Karınca Aslanı Optimizasyonu (Ant Lion Optimization, ALO) ile geliştirilmiş MLP sinir ağı olmak üzere iki farklı model geliştirilmiş ve tasarlanan ileti dizi birim hücre elemanına ait benzetim verileriyle eğitilmiştir. Modellerin performansını değerlendirmek amacıyla kök ortalama kare hata (RMSE) değerleri hesaplanmış ve her iki modelin başarısı karşılaştırılmıştır. Böylece tasarlanan birim hücreye ait tasarım parametreleri ve iletim fazı için yüksek doğruluklu bir model geliştirilmiştir. Çalışma, özellikle 5G ve sonraki nesil iletişim sistemlerinde ileti dizi birim hücre tasarımında yenilikçi ve etkili bir yaklaşım sunmakta, ayrıca yapay zekâ ve meta-sezgisel optimizasyon tekniklerinin entegrasyonunun elektromanyetik yapıların tasarım sürecindeki potansiyeli ortaya koymaktadır.
Abstract
In the first step of the presented study, a unit cell of the transmitarray antenna is designed, and the transmission phase characteristics are obtained by considering the design parameters. In the second step of the study, two different models, the Multilayer Perceptron (MLP) and AntLion Optimizer (ALO), an enhanced MLP neural network, are developed and trained using the simulated data of the designed unit cell element of the transmitarray. In order to evaluate the performance of the models, root mean square error (RMSE) values were calculated, and the performance of both models was compared. Thus, a high accuracy model was developed for the design parameters and transmission phase of the designed unit cell. The study presents an innovative and effective approach to the design of the unit cell of transmitarray, especially in 5G beyond communication systems, and highlights the potential of optimization and machine learning integration.
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Kaynakça (35)
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