Umut ÖZKAYA
(Konya Teknik Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik-Mühendislik, Konya, Türkiye.)
Levent SEYFİ
(Konya Teknik Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik-Mühendislik, Konya, Türkiye.)
Saban OZTURK
(Amasya Üniversitesi, Teknoloji Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü, Amasya, Türkiye.)
Yıl: 2021Cilt: 27Sayı: 2ISSN: 2147-5881Sayfa Aralığı: 229 - 233İngilizce

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Dimension optimization of multi-band microstrip antennas using deep learning methods
The electromagnetic frequency spectrum is divided into different sub frequency bands. These sub-frequency bands are allocated for different applications. In these days, devices operating in multiple sub-frequency bands provide significant advantages. Devices require antenna structures to operate in multiple frequency bands. Microstrip antennas have become prominent antenna structures with their small size, portable structures and easy integration into other systems. In this study, microstrip antenna structure which can work in multi frequency bands is designed. At the same time, it was used with deep learning methods in optimization of antenna sizes to ensure the optimization of the designed antenna in a shorter time. The operating frequencies of designed antenna structure work in the C and X band as seen in the obtained results. According to IEEE standards, C band is determined between 4 GHz and 8 GHz; X band determined as in 8 GHz and 12 GHz frequency range. In the proposed antenna structure, the ability to operate in multi-band structures was achieved by means of a C-shaped antenna array. In the deep learning methods that will be used in the optimization process, five different Long Short Term Memory (LSTM) models are used. The most important advantage of deep learning methods is that it can achieve satisfactory results by identifying the necessary features for solving difficult and time consuming problems with its own learning ability. In this context, 52 pieces of antenna data were produced. 40 pieces of data were used in the training process and 12 pieces of data were used in the test stage. The lowest root mean square error (RMSE) performance obtained in the test data was determined as LSTM-1 + Dropout layer-1 + LSTM -2 + Dropout layer-2 and 1.0161 error value. The obtained results by proposed method were evaluated in High Frequency Simulation Software (HFSS) program. In experimental results, it was observed that the results produced by the deep learning model and the test data were very close to each other.
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