Rekabetçi Evrimsel Algoritmalar ile Yuvarlak Papyon Anten Tasarımı

Uluslu, Ahmet

doi:10.24012/dumf.1001870

DÜMF Mühendislik Dergisi

2021

DOI: 10.24012/dumf.1001870

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Rekabetçi Evrimsel Algoritmalar ile Yuvarlak Papyon Anten Tasarımı

Ahmet Uluslu¹

Abstract: Bu çalışmada kablosuz yerel alan ağları (WLAN) uygulamaları için 5 GHz bandındaki yuvarlak papyon antenin tasarım parametrelerinin seçimi rekabetçi evrimsel algoritmalar ile çok boyutlu ve çok amaçlı bir tasarım optimizasyon problemi olarak sunulmuştur. Bu zorlu tasarım optimizasyonun üstesinden gelebilmek için yeni, hızlı ve güçlü optimizasyon algoritmaları, ateşböceği, diferansiyel evrim ve guguk kuşu arama kullanılarak anten optimizasyon işlemi rekabetçi bir biçimde karşılaştırılmıştır. Önerilen anten tasar… Show more

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2022

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Farkli Örnekleme Yöntemleri̇ni̇n Yapay Si̇ni̇r Ağlarinda Performansinin Karşilaştirilmasi

Uluslu

2022

Mühendislik Bilimleri Ve Tasarım Dergisi

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In many modeling problems, the selection of the data to be used is of great importance. In this study, the performances of different sampling methods for artificial neural network modeling were compared on the microstrip patch antenna by using a multilayer perceptron, which is the model that is frequently used in antenna designs. The selected artificial neural network model consists of 5 input and 1 output parameters. The application of Latin Hypercube and Monte Carlo sampling method in the modeling problem for microstrip patch antenna design is investigated. First of all, training and test data sets are provided according to their unique creation method for Latin Hypercube and Monte Carlo examples. In the performance comparison, a total of 12 different networks with 3 algorithms and 4 different architectural structures and 4 different training and test data sets were used. When the results were compared and analyzed with each other, it was seen that the Monte Carlo sampling method gave more successful results in terms of performance in both small and large sample numbers. On the other hand, it was seen that the Latin Hypercube sampling method, on the other hand, increased the number of samples and caused partial improvement. However, it still lags behind the other Monte Carlo sampling method, which has less sample size, in terms of performance. Therefore, it was concluded that Monte Carlo sampling method is more applicable for this problem.

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Farkli Örnekleme Yöntemleri̇ni̇n Yapay Si̇ni̇r Ağlarinda Performansinin Karşilaştirilmasi

Uluslu

2022

Mühendislik Bilimleri Ve Tasarım Dergisi

Self Cite

View full text Add to dashboard Cite

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Rekabetçi Evrimsel Algoritmalar ile Yuvarlak Papyon Anten Tasarımı

Cited by 1 publication

References 27 publications

Farkli Örnekleme Yöntemleri̇ni̇n Yapay Si̇ni̇r Ağlarinda Performansinin Karşilaştirilmasi

Farkli Örnekleme Yöntemleri̇ni̇n Yapay Si̇ni̇r Ağlarinda Performansinin Karşilaştirilmasi

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