Design and experimental validation of customised fractal FSS

ABSTRACT

The present work deals with the combination of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) for the development of a flexible, time-efficient CAD module for design of customised Fractal Frequency Selective Surfaces (FSS). The design task of FSS is approached as an optimization problem and solved using PSO whose fitness function is evaluated using a previously trained ANN to estimate the design parameters of the unit cell of fractal FSS for multiple frequencies as per the user’s demand. The results of the developed approach are cross-verified with experimental results. The developed approach can easily be extended for the design of other multifrequency structures including metasurfaces.

KEYWORDS:

• Frequency selective surfaces (FSS)
• particle swarm optimization (PSO)
• artificial neural network (ANN)

Disclosure statement

No potential conflict of interest was reported by the authors.