Design of frequency selective surface comprising of dipoles using artificial neural network

Monojit Rudra (University of Kalyani)
P Soni Reddy (University of Kalyani)
Rajatsubhra Chakraborty (Future Institute of Engineering and Management)
Partha Pratim Sarkar (University of Kalyani)

International Journal of Advances in Applied Sciences
Vol 9, No 4: December 2020

This paper depicts the design of Frequency Selective Surface (FSS) comprising of dipoles using Artificial Neural Network (ANN). It has been observed that with the change of the dimensions and periodicity of FSS, the resonating frequency of the FSS changes. This change in resonating frequency has been studied and investigated using simulation software. The simulated data were used to train the proposed ANN models. The trained ANN models are found to predict the FSS characteristics precisely with negligible error. Compared to traditional EM simulation softwares (like ANSOFT Designer), the proposed technique using ANN models is found to significantly reduce the FSS design complexity and computational time. The FSS simulations were made using ANSOFT Designer v2 software and the neural network was designed using MATLAB software.