E ŞEKİLLİ YAMA ANTENLERİN ÇALIŞMA FREKANSININ HESAPLANMASI İÇİN FARKLI ÖĞRENME ALGORİTMALARI İLE EĞİTİLMİŞ BİR YAPAY SİNİR AĞI TASARIMI
Abstract
Bu
çalışmada, E şekilli yama antenlerin (EŞYA) çalışma frekansının hesaplanması
için uygulanmış farklı öğrenme algoritmaları ile eğitilmiş bir yapay sinir ağı
(YSA) tasarlanmıştır. YSA modeli, ileri beslemeli geri yayılım temelli çok
katmanlı algılayıcı (ÇKA) üzerine inşa edilmiştir. YSA modelinin eğitilmesi ve
test edilmesi için 144 adet EŞYA’nın benzetimi, çalışma frekansı yönünden moment
metoduna dayanan HyperLynx® 3D EM yazılımı kullanarak yapılmıştır. Daha sonra,
YSA modeli, benzetimi yapılan 144 EŞYA verisinden 130’u aracılığıyla eğitilmiş
ve modelin doğruluğu 14 veri üzerinden test edilmiştir. Güçlü bir model elde
etmek için YSA, 8 farklı öğrenme algoritması ile eğitilmiştir. Öğrenme
algoritmalarını yüzdelik hata oranına göre birbirleri ile karşılaştıran bir
sıralama çizelgesi sunulmuştur. YSA’nın geçerliliği, literatürde verilmiş
benzetim ve ölçüm verileri ile doğrulanmıştır. Bu sonuçlar, Levenberg–Marquardt
öğrenme algoritması ile eğitilmiş YSA modelinin en yakın sonuçları hesapladığı
gösterilmiştir. Önerilen YSA modeli, çalışma frekansı bakımından EŞYA’ların
analizinde başarılı bir şekilde kullanılabilir.
References
- Garg, R. Bhartia, P. Bahl, I. and Ittipiboon, A. (2001) Microstrip antenna design handbook, Londra, Artech House.
- Toktas, A. and Akdagli, A. (2012) Computation of operating frequency of E-shaped compact microstrip antennas, Journal of the Faculty of Engineering and Architecture of Gazi University, 27(4), 847-854. doi: 10.17341/gummfd.02944
- Deshmukh, A.A. Phatak, N.V. Nagarbovdi, S. and Ahuja, R. (2013) Analysis of broadband E-shaped microstrip antennas, International Journal of Computer Applications, 80(7), 24-29. doi: 10.5120/13874-1743
- Sagiroglu, S. and Guney, K. (1997) Calculation of resonant frequency for an equilateral triangular microstrip antenna with the use of artificial neural networks, Microwave and Optical Technology Letters, 14(2), 89-93. doi:10.1002/(SICI)1098-2760(19970205)14:2<89::AID-MOP5>3.0.CO;2-H
- Guney, K. and Sarikaya, N. (2007) Adaptive neuro-fuzzy inference system for computing the resonant frequency of electrically thin and thick rectangular microstrip antennas, International Journal of Electronics, 94(9), 833-844. doi:10.1080/00207210701526317
- Malathi, P. and Kumar, R. (2009) On the design of multilayer circular microstrip antenna using artificial neural network, International Journal of Recent Trends in Engineering, 2(5), 70-74.
- Dadgarnia, A. and Heidari, A. A. (2010) A fast systematic approach for microstrip antenna design and optimization using ANFIS and GA, Journal of Electromagnetic Waves and Applications, 24(16), 2207-2221. doi: 10.1163/156939310793699037
- Venmathi, A. R. and Vanitha, L. (2011) Support vector machine for bandwidth analysis of slotted microstrip antenna, International Journal of Computer Science, Systems Engineering and Information Technology, 4(1), 67-61.
- Kayabasi, A. and Akdagli, A. (2016) Usage of ANN and ANFIS methods for computing resonant frequency of slot-loaded compact microstrip antennas, Journal of the Faculty of Engineering and Architecture of Gazi University, 31(1), 105-117. doi: 10.17341/gummfd.71495
- Hagan, M. T. and Menhaj, M. (1994) Training feed forward networks with the Marquardt algorithm, IEEE Transactions on Neural Network, 5(6), 989-99. doi: 10.1109/72.329697
- Caddemi, A. Donato, N. and Xibilia, M. G. (2003) Advanced simulation of semiconductor devices by artificial neural networks, Journal of Computational Electronics, 2(2), 301–307. doi: 10.1023/B:JCEL.0000011442.17774.cf
- Zandieh, M. Azadeh, A. Hadadi, B. and Saberi, M. (2009) Application of neural networks for airline number of passenger estimation in time series state, Journal of Applied Sciences, 9(6), 1001-1013. doi: 10.3923/jas.2009.1001.1013
- Harrington, R. F. (1993) Field computation by moment methods, Piscataway, IEEE Press, New Jersey.
Design of An Ann Model Trained by Various Learning Algorithms to Compute the Operating Frequency of E-Shaped Patch Antennas
Abstract
An artificial neural network (ANN) trained by
different learning algorithms implemented to computing the operating frequency
of E-shaped patch antennas (EPAs) is designed in this study. The ANN model is
built on a multilayered perceptron (MLP) based on feed forward back propagation
(FFBP). A data pool is firstly constituted for training and testing the ANN
model through 144 EPA simulations using the moment method-based HyperLynx® 3D
EM software in terms of the operating frequency. The ANN model is then trained
via 130 data, and the accuracy of the model is tested through 14 data of
simulated EPAs. The ANN is trained by 8 different learning algorithms to
achieve a robust model. A benchmark which compares the learning algorithms
against each other according to percentage error is revealed. The validity of
the ANN is corroborated by simulated and measured data reported in the
literature. It shows that the ANN model trained by Levenberg–Marquardt learning
algorithm computes the closest results. The proposed ANN model can be
successfully exploited to analyze the EPAs in views of the operating frequency.
Keywords
Antennas patch antennas artificial neural networks (ANN) operating frequency learning algorithms
References
- Garg, R. Bhartia, P. Bahl, I. and Ittipiboon, A. (2001) Microstrip antenna design handbook, Londra, Artech House.
- Toktas, A. and Akdagli, A. (2012) Computation of operating frequency of E-shaped compact microstrip antennas, Journal of the Faculty of Engineering and Architecture of Gazi University, 27(4), 847-854. doi: 10.17341/gummfd.02944
- Deshmukh, A.A. Phatak, N.V. Nagarbovdi, S. and Ahuja, R. (2013) Analysis of broadband E-shaped microstrip antennas, International Journal of Computer Applications, 80(7), 24-29. doi: 10.5120/13874-1743
- Sagiroglu, S. and Guney, K. (1997) Calculation of resonant frequency for an equilateral triangular microstrip antenna with the use of artificial neural networks, Microwave and Optical Technology Letters, 14(2), 89-93. doi:10.1002/(SICI)1098-2760(19970205)14:2<89::AID-MOP5>3.0.CO;2-H
- Guney, K. and Sarikaya, N. (2007) Adaptive neuro-fuzzy inference system for computing the resonant frequency of electrically thin and thick rectangular microstrip antennas, International Journal of Electronics, 94(9), 833-844. doi:10.1080/00207210701526317
- Malathi, P. and Kumar, R. (2009) On the design of multilayer circular microstrip antenna using artificial neural network, International Journal of Recent Trends in Engineering, 2(5), 70-74.
- Dadgarnia, A. and Heidari, A. A. (2010) A fast systematic approach for microstrip antenna design and optimization using ANFIS and GA, Journal of Electromagnetic Waves and Applications, 24(16), 2207-2221. doi: 10.1163/156939310793699037
- Venmathi, A. R. and Vanitha, L. (2011) Support vector machine for bandwidth analysis of slotted microstrip antenna, International Journal of Computer Science, Systems Engineering and Information Technology, 4(1), 67-61.
- Kayabasi, A. and Akdagli, A. (2016) Usage of ANN and ANFIS methods for computing resonant frequency of slot-loaded compact microstrip antennas, Journal of the Faculty of Engineering and Architecture of Gazi University, 31(1), 105-117. doi: 10.17341/gummfd.71495
- Hagan, M. T. and Menhaj, M. (1994) Training feed forward networks with the Marquardt algorithm, IEEE Transactions on Neural Network, 5(6), 989-99. doi: 10.1109/72.329697
- Caddemi, A. Donato, N. and Xibilia, M. G. (2003) Advanced simulation of semiconductor devices by artificial neural networks, Journal of Computational Electronics, 2(2), 301–307. doi: 10.1023/B:JCEL.0000011442.17774.cf
- Zandieh, M. Azadeh, A. Hadadi, B. and Saberi, M. (2009) Application of neural networks for airline number of passenger estimation in time series state, Journal of Applied Sciences, 9(6), 1001-1013. doi: 10.3923/jas.2009.1001.1013
- Harrington, R. F. (1993) Field computation by moment methods, Piscataway, IEEE Press, New Jersey.
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | November 28, 2016 |
| Submission Date | March 28, 2016 |
| Acceptance Date | December 26, 2016 |
| Published in Issue | Year 2016 Volume: 21 Issue: 2 |
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