Minkowski, Giuseppe Peano and Koch curves based design of compact hybrid fractal antenna for biomedical applications using ANN and PSO

Abstract

In this work, an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) techniques are used to compute the feed position of projected Minkowski, Giuseppe Peano and Koch curves based hybrid fractal Antenna (MGKA). The structure of projected MGKA is obtained by the combination of three different fractal geometries i.e. Minkowski, Giuseppe Peano and Koch. Different fractal geometries are combined to enhance the performance characteristics and to achieve better miniaturization. The projected MGKA consists of 1.6 mm thick, low cost FR4 substrate whose relative permittivity is 4.4 and loss tangent value is 0.02. A set of 70 MGKAs with different dimensions is designed to obtain a data dictionary for the implementation of ANN and PSO techniques. The prototype is fabricated and then tested with Vector Network Analyzer (VNA). From the measured results, it is depicted that the resonant frequencies are 2.44 GHz and 5.84 GHz along with S (1, 1) values −18.23 dB and −12.71 dB. The fabricated prototype also provides excellent gains of 4.69 dB and 15.11 dB and operates within the Industrial, Scientific and Medical (ISM) frequency bands i.e. (2.4–2.5 GHz) and (5.82–5.85 GHz). The results of proposed ANN model and PSO techniques are well matched with the simulated and experimental values.