Artificial Neural Network Model for Evaluating Parameters of Reflection-Asymmetric Samples From Reference-Plane-Invariant Measurements

Hasar U. C., Ozturk H., Ertugrul M., Barroso J. J., Ramahi O. M.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, vol.72, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 72
  • Publication Date: 2023
  • Doi Number: 10.1109/tim.2023.3273664
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Karadeniz Technical University Affiliated: No


A technique based on artificial neural network (ANN) is proposed to extract the electromagnetic properties of reflection-asymmetric samples from reference-plane-invariant (RPI) scattering parameter measurements. It first determines reference plane transformation distances and then extracts the material properties. The number of neurons in the hidden layer of the ANN model was evaluated subject to accuracy and time constraints. We examined the conformity of the dataset of the ANN model and the required time for the training process by considering different numbers of neurons in the selected hidden layer. S -parameter waveguide measurements at the X -band (8.2-12.4 GHz) of two bianisotropic metamaterial (MM) slabs, as reflection-asymmetric samples, composed of square-shaped split ring resonators (SRRs) and asymmetrically positioned into their measurement cells were used to validate the ANN model and evaluate the effectiveness of the proposed method in extracting the electromagnetic properties.