Applications of ANN and ANFIS to Predict the Resonant Frequency of L-Shaped Compact Microstrip Antennas

Authors

  • Ahmet Kayabasi Department of Electronics and Automation Silifke-Tasucu Vocational School of Selcuk University, Silifke, Mersin, Turkey
  • Abdurrahim Toktas Department of Information Technologies Mersin University, Ciftlikkoy, Yenisehir, 33343, Mersin, Turkey
  • Ali Akdagli Department of Electrical–Electronics Engineering Mersin University, Ciftlikkoy, Yenisehir, 33343, Mersin, Turkey
  • Mustafa B. Bicer Department of Electrical–Electronics Engineering Mersin University, Ciftlikkoy, Yenisehir, 33343, Mersin, Turkey
  • Deniz Ustun Department of Software Engineering Mersin University, Tarsus, 33400, Mersin, Turkey

Keywords:

Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), compact microstrip antenna, Lshaped compact microstrip antenna, resonant frequency.

Abstract

Since the Compact Microstrip Antennas (CMAs) with various shapes are crucial for mobile communication, they take much attention in present days and studies related to analysis and design on them have been increasing day by day. In this work, simple approaches based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for computing the resonant frequency of L-shaped CMAs operating at UHF band have been presented. In order to train and test the ANN and ANFIS models, 192 LCMAs having different physical dimensions and relative dielectric constants were simulated by electromagnetic simulation software named IE3D™, which is based on Method of Moment (MoM). 172 of LCMAs were employed for training, while the remainders were utilized for testing the models. Average Percentage Errors (APEs) for training were obtained as 0.345% and 0.090% for ANN and ANFIS models, respectively. The constructed models were then tested over the test data and APEs values were achieved as 0.537% for ANN and 0.454% for ANFIS. Afterwards, the accuracy and validity of ANN and ANFIS models proposed in this work were verified on measurement data of the fabricated LCMAs. The results indicate that ANN and ANFIS can be successfully used to predict the resonant frequency of LCMAs without necessitating any other sophisticated calculations.

Downloads

Download data is not yet available.

References

R. Garg, P. Bhartia, I. Bahl, and A. Ittipiboon, “Microstrip antenna design handbook,” Artech House, Londra, 2001.

K. L. Wong, “Compact and broadband microstrip atennas,” John Wiley & Sons, Inc., 2002.

G. Kumar and K. P. Ray, “Broadband microstrip antennas,” Artech House, USA, 2003.

A. A. Deshmukh and G. Kumar, “Formulation of resonant frequency for compact rectangular microstrip antennas,” Microw. Opt. Techn. Let., vol. 49, pp. 498-501, 2007.

F. Yang, X. X. Zhang, X. Ye, and Y. RahmatSamii, “Wide-band e-shaped patch antennas for wireless communications,” IEEE Trans. Antennas Propag., vol. 49, pp. 1094-1110, 2001.

A. F. Sheta, A. Mohra, and S. F. Mahmoud, “Multi-band operation of a compact h-shaped microstrip antenna,” Microw. Opt. Techn., vol. 35, pp. 363-367, 2002.

Z. N. Chen, “Radiation pattern of a probe fed lshaped plate antenna,” Microw. Opt. Techn. Let., vol. 27, pp. 410-413, 2000.

W. F. Richards, Y. T. Lo, and D. D. Harrisson, “An improved theory for microstrip antennas and applications,” IEEE T. Antenn. Propag., vol. 29, pp. 38-46, 1981.

K. Bhattacharyya and R. Garg, “A generalized transmission line model for microstip patches,” IEEE Proc. Microwave Antennas Propag., vol. 132, pp. 93-98, 1985.

A. Taflove, “Computational electrodynamics: the finite-difference time domain method,” Artech House, Boston, 1995.

R. F Harrington, “Field computation by moment methods,” IEEE Press, Piscataway, NJ, 1993.

M. Paulson, S. O. Kundukulam, C. K Aanandan, and P. Mohanan, “Resonance frequencies of compact microstrip antenna,” Electron. Lett., vol. 37, pp. 1151-1153, 2001.

D. K. Neog and R. Devi, “Determination of resonant frequency of slot-loaded rectangular microstrip patch antennas,” Microw. Opt. Techn. Let., vol. 52, pp. 446-448, 2010.

A. Akdagli and A. Toktas, “A novel expression in calculating resonant frequency of h-shaped compact microstrip antennas obtained by using artificial bee colony algorithm,” J. Electromagnet. Wave, vol. 24, pp. 2049-2061, 2010.

A. Toktas, A. Akdagli, M. B. Bicer, and A. Kayabasi, “Simple formulas for calculating resonant frequencies of c and h shaped compact microstrip antennas obtained by using artificial bee colony algorithm,” J. Electromagnet. Wave, vol. 25, pp. 1718-1729, 2011.

A. Toktas and A. Akdagli, “Computation of resonant frequency of e-shaped compact microstrip antennas,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 27, pp. 847-854, 2012.

S. Haykin, “Neural networks: a comprehensive foundation,” Macmillan College Publishing Company, New York, A.B.D, 1994.

J. S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE T. Syst. Man. Cy., vol. 23, pp. 665-685, 1993.

K. Guney and N. Sarikaya, “Adaptive neurofuzzy inference system for computing the resonant frequency of electrically thin and thick rectangular microstrip antennas,” International Journal of Electronics, vol. 94, pp. 833-844, 2007.

Y. B. Tian and Z. B. Xie, “Particle-swarmoptimization-based selective neural network ensemble and its application to modeling resonant frequency of microstrip antenna,” Microstrip Antennas, InTech, 2011.

S. Sagiroglu and K. Guney, “Calculation of resonant frequency for an equilateral triangular microstrip antenna with the use of artificial neural networks,” Microw. Opt. Techn. Let., vol. 14, pp. 89-93, 1997.

K. Guney and S. S. Gultekin, “Artificial neural networks for resonant frequency calculation of rectangular microstrip antennas with thin and thick substrates,” Int. J. Infrared Milli., vol. 25, pp. 1383-1399, 2004.

H. J. Delgado, “A novel neural network the synthesis of antennas and microwave devices,” IEEE T. Neural Networ., vol. 16, pp. 1590-1600, 2005.

E. B. Rahouyi, J. Hinojosa, and J. Garrigós, “Neuro-fuzzy modeling techniques for microwave components,” IEEE Microw. Wirel. Co., vol. 16, pp. 72-74, 2006.

J. Hinojosa and G. Doménech-Asensi, “Spacemapped neuro-fuzzy optimization for microwave device modeling,” Microw. Opt. Techn. Let., vol. 49, pp. 1328-1334, 2007.

M. Turkmen, S. Kaya, C. Yildiz, and K. Guney, “Adaptive neuro-fuzzy models for conventional coplanar waveguide,” Prog. Electromagn. Res., vol. 6, pp. 93-107, 2008.

P. Malathi and R. Kumar, “On the design of multilayer circular microstrip antenna using artificial neural network,” International Journal of Recent Trends in Engineering, vol. 2, pp. 70- 74, 2009.

A. Dadgarnia and A. A. Heidari, “A fast systematic approach for microstrip antenna design and optimization using ANFIS and GA,” J. Electromagnet. Wave, vol. 24, pp. 2207-2221, 2010.

T. Khan and A. De, “Computation of different parameters of triangular patch microstrip antennas using a common neural model,” International Journal of Microwave and Optical Technology, vol. 5, no. 4, pp. 219-224, 2010.

A. Kayabasi, M. B. Bicer, A. Akdagli, and A Toktas, “Computing resonant frequency of hshaped compact microstrip antennas operating at UHF band by using artificial neural networks.” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 26, pp. 833- 840, 2011.

M. Pandit and T. B. Roy, “Artificial models for determining antenna parameters for a resonant frequency,” International Journal of Current Engineering and Technology, vol. 3, no. 2, pp. 297-302, 2013.

R. Ghayoula, N. Fadlallah, A. Gharsallah, and M. Rammal, “Design, modelling, and synthesis of radiation pattern of intelligent antenna by artificial neural networks,” Applied Computational Electromagnetics Society (ACES) Journal, vol. 23, no. 4, pp. 336-344, December 2008.

Y. Xiong, D. G. Fang, and R. S. Chen, “Application of two-dimensional AWE algorithm in training multi-dimensional neural network model,” Applied Computational Electromagnetics Society (ACES) Journal, vol. 18, no. 2, pp. 64-71, July 2003.

J. H. Holland, “Adaptation in natural and artificial systems,” University of Michigan Press, Ann Arbor, 1975.

M. T. Hagan and M. Menhaj, “Training feedforward networks with the marquardt algorithm,” IEEE T. Neural Networ., vol. 5, pp. 989-993, 1994.

J. S. R. Jang, “Self-learning fuzzy controllers based on temporal backpropagation,” IEEE T. Neural Networ., vol. 3, pp. 714-723, 1992.

K. Guney and N. Sarikaya, “Adaptive neurofuzzy inference system for computing patch radius of circular microstrip antennas,” Microw. Opt. Techn. Let., vol. 48, pp. 1606-1610, 2006.

Downloads

Published

2021-09-03

How to Cite

[1]
A. . Kayabasi, A. . Toktas, A. . Akdagli, M. B. . Bicer, and D. . Ustun, “Applications of ANN and ANFIS to Predict the Resonant Frequency of L-Shaped Compact Microstrip Antennas”, ACES Journal, vol. 29, no. 06, pp. 460–469, Sep. 2021.

Issue

Section

General Submission