Notch Antenna Analysis: Artificial Neural Network-based Operating Frequency Estimator


Sabanci K., Kayabasi A., Toktas A., Yigit E.

APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL, vol.32, no.4, pp.303-309, 2017 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 4
  • Publication Date: 2017
  • Journal Name: APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.303-309

Abstract

An artificial neural network (ANN) based estimator is presented for notch antenna analysis in terms of the operating frequency. The notch antenna is formed by loading an asymmetric slot on one side of a rectangular patch. Architecture of the estimator is constructed over an ANN model trained with the simulated data of the notch antennas. In order to constitute a data pool for training and testing the ANN model, 96 notch antennas with seven antenna parameters are simulated with respect to the operating frequency. Antenna parameters including the patch dimensions, height and relative permittivity of the substrate are used as input vector of the ANN model. The simulated data of 80 notch antennas are employed to train the ANN model. The estimator is corroborated through testing with the remainders 16 antenna data, verifying with antenna data in the literature and validating with a prototyped notch antenna data. The results show that the estimator simply and fast computes the operating frequency of the notch antennas in very close to real one without performing simulations or measurement.