A comparative study on parameters of leaf-shaped patch antenna using hybrid artificial intelligence network models

• Published in: Neural computing & applications 2018-04, Vol.29 (8), p.35-45
• Main Authors: Ozkaya, Umut, Seyfi, Levent
• Format: Article
• Language: English
• Subjects: Antenna design; Antennas; Artificial intelligence; Artificial neural networks; Bandwidths; Evolutionary algorithms; Hybrid systems; Mathematical models; Neural networks; Parameters; Particle swarm optimization; Patch antennas; Search algorithms; Substrates; Swarm intelligence
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-016-2620-1

This study proposes a very compact coaxial-fed planar antenna for X band applications. The antenna design includes a tulip-shaped radiator on the FR4 dielectric substrate. The antenna parameters, such as return losses, bandwidth and operating frequency, have close relationships with patch geometry. In order to obtain desired antenna parameters for X band application, patch dimension is necessary to be optimized. In this article, four different hybrid artificial intelligence network models are suggested for optimization. These are particle swarm optimization, differential evolution, grey wolf optimizer and vortex search algorithm. Also, they are combined with artificial neural network for the purpose of estimating dimension of patch. Therefore, the comparison of different proposed algorithms is analyzed to obtain higher characteristics for antenna design. Their results are compared with each other in HFSS 13.0 software. The antenna with the most suitable return loss, bandwidth and operating frequency is selected to be used in antenna design.