Design of an Ann Model Trained by Various Learning Algorithms to Compute the Operating Frequency of E-Shaped Patch Antennas

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An artificial neural network (ANN) trained by different learning algorithms implemented to computing the operating frequency of E-shaped patch antennas (EPAs) is designed in this study. The ANN model is built on a multilayered perceptron (MLP) based on feed forward back propagation (FFBP). A data pool is firstly constituted for training and testing the ANN model through 144 EPA simulations using the moment method-based HyperLynx® 3D EM software in terms of the operating frequency. The ANN model is then trained via 130 data, and the accuracy of the model is tested through 14 data of simulated EPAs. The ANN is trained by 8 different learning algorithms to achieve a robust model. A benchmark which compares the learning algorithms against each other according to percentage error is revealed. The validity of the ANN is corroborated by simulated and measured data reported in the literature. It shows that the ANN model trained by Levenberg–Marquardt learning algorithm computes the closest results. The proposed ANN model can be successfully exploited to analyze the EPAs in views of the operating frequency.


Antennas; patch antennas; artificial neural networks (ANN); operating frequency; learning algorithms

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

Toktas, A. and Akdagli, A. (2012) Computation of operating frequency of E-shaped compact microstrip antennas, Journal of the Faculty of Engineering and Architecture of Gazi University, 27(4), 847-854. doi: 10.17341/gummfd.02944

Deshmukh, A.A. Phatak, N.V. Nagarbovdi, S. and Ahuja, R. (2013) Analysis of broadband E-shaped microstrip antennas, International Journal of Computer Applications, 80(7), 24-29. doi: 10.5120/13874-1743

Sagiroglu, S. and Guney, K. (1997) Calculation of resonant frequency for an equilateral triangular microstrip antenna with the use of artificial neural networks, Microwave and Optical Technology Letters, 14(2), 89-93. doi:10.1002/(SICI)1098-2760(19970205)14:2<89::AID-MOP5>3.0.CO;2-H

Guney, K. and Sarikaya, N. (2007) Adaptive neuro-fuzzy inference system for computing the resonant frequency of electrically thin and thick rectangular microstrip antennas, International Journal of Electronics, 94(9), 833-844. doi:10.1080/00207210701526317

Malathi, P. and Kumar, R. (2009) On the design of multilayer circular microstrip antenna using artificial neural network, International Journal of Recent Trends in Engineering, 2(5), 70-74.

Dadgarnia, A. and Heidari, A. A. (2010) A fast systematic approach for microstrip antenna design and optimization using ANFIS and GA, Journal of Electromagnetic Waves and Applications, 24(16), 2207-2221. doi: 10.1163/156939310793699037

Venmathi, A. R. and Vanitha, L. (2011) Support vector machine for bandwidth analysis of slotted microstrip antenna, International Journal of Computer Science, Systems Engineering and Information Technology, 4(1), 67-61.

Kayabasi, A. and Akdagli, A. (2016) Usage of ANN and ANFIS methods for computing resonant frequency of slot-loaded compact microstrip antennas, Journal of the Faculty of Engineering and Architecture of Gazi University, 31(1), 105-117. doi: 10.17341/gummfd.71495

Hagan, M. T. and Menhaj, M. (1994) Training feed forward networks with the Marquardt algorithm, IEEE Transactions on Neural Network, 5(6), 989-99. doi: 10.1109/72.329697

Caddemi, A. Donato, N. and Xibilia, M. G. (2003) Advanced simulation of semiconductor devices by artificial neural networks, Journal of Computational Electronics, 2(2), 301–307. doi: 10.1023/

Zandieh, M. Azadeh, A. Hadadi, B. and Saberi, M. (2009) Application of neural networks for airline number of passenger estimation in time series state, Journal of Applied Sciences, 9(6), 1001-1013. doi: 10.3923/jas.2009.1001.1013

Harrington, R. F. (1993) Field computation by moment methods, Piscataway, IEEE Press, New Jersey.

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