Giuseppe Peano and Cantor set fractals based miniaturized hybrid fractal antenna for biomedical applications using artificial neural network and firefly algorithm

Abstract

In this research paper, Giuseppe Peano and Cantor set fractals based miniaturized hybrid fractal antenna (GCHFA) is proposed that operates for biomedical applications. The proposed GCHFA is designed by merging Giuseppe Peano and Cantor set fractals that help in achieving better performance characteristics as well as miniaturization. Firefly algorithm (FA) has been employed to optimize the feed position of the designed antenna. The substrate material selected for the proposed GCHFA is low-cost, commercially available FR4 epoxy whose thickness is 1.6 mm and relative permittivity is 4.4. A data set of 65 GCHFAs with different geometrical parameters is generated for the realization of two different bioinspired approaches. For the performance evaluation of fabricated prototype, vector network analyzer is used. The experimentally observed resonant frequencies are 2.4400 and 5.8115 GHz, and at these resonant frequencies, S (1,1) < −10 dB. The designed antenna is suitable for Industrial, Scientific, and Medical bands of biomedical applications. Moreover, the behavior of the proposed GCHFA is nearly omnidirectional. A comparative study of three different artificial neural networks (ANNs) is also done to evaluate the most suitable ANN type for the analysis of proposed GCHFA. The optimized, simulated, and experimental results depict that they are closely matched.