AN APPLICATION OF ARTIFICIAL NEURAL NETWORK TO COMPUTE THE RESONANT FREQUENCY OF E-SHAPED COMPACT MICROSTRIP ANTENNAS

Akdagli A., Toktaş A., Kayabasi A., Develi İ.

JOURNAL OF ELECTRICAL ENGINEERING-ELEKTROTECHNICKY CASOPIS, cilt.64, sa.5, ss.317-322, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 64 Sayı: 5
  • Basım Tarihi: 2013
  • Doi Numarası: 10.2478/jee-2013-0046
  • Dergi Adı: JOURNAL OF ELECTRICAL ENGINEERING-ELEKTROTECHNICKY CASOPIS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.317-322
  • Anahtar Kelimeler: compact microstrip antenna, E-shaped antenna, resonant frequency, artificial neural network (ANN), BAND
  • Ankara Üniversitesi Adresli: Evet

Özet

An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip antennas (ECMAs) is presented in this paper. The resonant frequencies of 144 ECMAs with different dimensions and electrical parameters were firstly determined by using IE3D((tm)) software based on the method of moments (MoM), then the ANN model for computing the resonant frequency was built by considering the simulation data. The parameters and respective resonant frequency values of 130 simulated ECMAs were employed for training and the remaining 14 ECMAs were used for testing the model. The computed resonant frequencies for training and testing by ANN were obtained with the average percentage errors (APE) of 0.257% and 0.523%, respectively. The validity and accuracy of the present approach was verified on the measurement results of an ECMA fabricated in this study. Furthermore, the effects of the slots loading method over the resonant frequency were investigated to explain the relationship between the slots and resonant frequency.