Table of Contents Author Guidelines Submit a Manuscript
International Journal of Antennas and Propagation
Volume 2015, Article ID 165717, 11 pages
Research Article

Slot Parameter Optimization for Multiband Antenna Performance Improvement Using Intelligent Systems

1Turksat International Satellite Cable TV Operator, Golbasi, 06380 Ankara, Turkey
2Baskent University, Technical Science MYO, Baglica, 06810 Ankara, Turkey
3Electrics and Electronics Department, Ankara University, Golbasi, 06380 Ankara, Turkey
4Electrical and Electronics Engineering Department, Istanbul Commerce University, Kucukyali, 34840 Istanbul, Turkey

Received 13 August 2015; Revised 28 October 2015; Accepted 2 November 2015

Academic Editor: Ikmo Park

Copyright © 2015 Erdem Demircioglu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs) using symmetrical rectangular/square slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN). To achieve the best ANN performance, Particle Swarm Optimization (PSO) and Differential Evolution (DE) are applied with ANN’s conventional training algorithm in optimization of the modeling performance. In this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth enhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm SMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial intelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer 90% accuracy with lack of resonance frequency tracking.