A Simple and Efficient Approach to Compute the Operating Frequency of Annular Ring Patch Antennas by Using ANN with Bayesian Regularization Learning Algorithm

Ahmet KAYABAŞI; Ali AKDAĞLI

Makale detayları

A Simple and Efficient Approach to Compute the Operating Frequency of Annular Ring Patch Antennas by Using ANN with Bayesian Regularization Learning Algorithm

Yazarlar:

Ahmet KAYABAŞI | Ali AKDAĞLI

Yayıncı:

İsmail SARITAŞ

Ana Başlıklar:

Uygulamalı fizik

Dergi:

International Journal of Intelligent Systems and Applications in Engineering 2016 Vol.4 Issue Special Issue-1, pp.239-243

Belge Tipi:

Makale

Yayın Tarihi:

12/2016

Belge Dili:

Türkçe

Document ID (Org No.):

2000817543

Sıklık:

Üç Aylık

eISSN:

2147-6799

Özet:

 


An annular ring patch antenna (ARPA)
constructed by loading a circular slot in the center of the circular patch
antenna is a popular microstrip antenna due to its favourable properties. In
this paper, an application of artificial neural network (ANN) using bayesian
regularization (BR) learning algorithm based on multilayer perceptron (MLP)
model is presented for computing the operating frequency of annular ring ARPAs
in UHF band.  Firstly, the operating
frequencies of 80 ARPAs having varied dimensions and electrical parameters were
simulated with IE3DTM packaged software based on method of moment (MoM) in
order to generate the data set for training and testing processes of the ANN
model. Then ANN model was built with data set and while 70 simulated ARPAs and
remaining 10 simulated ARPAs were employed for ANN model training and testing
respectively. The proposed ANN model were confirmed by comparing with the
suggestions reported elsewhere via measurement data published earlier in the
literature. These results show that ANN model with BR learning algorithm can be
successfully used to compute the operating frequency of ARPAs. 


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Makale detayları

A Simple and Efficient Approach to Compute the Operating Frequency of Annular Ring Patch Antennas by Using ANN with Bayesian Regularization Learning Algorithm

Yazarlar: Ahmet KAYABAŞI | Ali AKDAĞLI

Yayıncı: İsmail SARITAŞ

Ana Başlıklar: Uygulamalı fizik

Dergi: International Journal of Intelligent Systems and Applications in Engineering 2016 Vol.4 Issue Special Issue-1, pp.239-243

Belge Tipi: Makale

Yayın Tarihi: 12/2016

Belge Dili: Türkçe

Document ID (Org No.): 2000817543

Sıklık: Üç Aylık

eISSN: 2147-6799

Sayı

Yazarlar:

Ahmet KAYABAŞI | Ali AKDAĞLI

Yayıncı:

İsmail SARITAŞ

Ana Başlıklar:

Uygulamalı fizik

Dergi:

International Journal of Intelligent Systems and Applications in Engineering 2016 Vol.4 Issue Special Issue-1, pp.239-243

Belge Tipi:

Makale

Yayın Tarihi:

12/2016

Belge Dili:

Türkçe

Document ID (Org No.):

2000817543

Sıklık:

Üç Aylık

eISSN:

2147-6799