Operating Frequency Estimation of Slot Antenna by Using Adapted kNN Algorithm
DOI:
https://doi.org/10.18201/ijisae.2018637927Keywords:
Slot antenna, antenna analysis, operating frequency, kNN algorithmAbstract
In this study ultra-high frequency slot antenna’s operating frequency is estimated by using adapted k-nearest neighbor (kNN) algorithm. kNN doesn’t use the training data points to do any generalization and it can be usually used for many classification. However, kNN can be adapted to estimate slot antenna’s operating frequency by assessing the best k-nearest value. To find the optimal k for operating frequency estimation, 96 slot antennas with seven antenna parameters are simulated with respect to the operating frequency by using a computational electromagnetic software. Antenna parameters includes the patch dimensions, height and relative permittivity of the substrate. The simulated 81 antennas are used to construct feature data pool and the residual 15 antennas are used to test kNN algorithm. The performance of the kNN is evaluated by comparing the output of operating frequency to the simulated one. Then the proposed model is corroborated with simulated antennas and validating with prototyped antenna data. The results shows that the kNN based model simply and fast computes the operating frequency of the slot antennas much close to real one without performing any simulations or measurement.
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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.
Z. N. Chen, “Radiation pattern of a probe fed L-shaped plate antenna,” Microw. Opt. Techn. Let., vol. 27, pp. 410-413, 2000.
A. Akdagli, A. Kayabasi and I Develi, “Computing resonant frequency of C-shaped compact microstrip antennas by using ANFIS,” Int. J. Elec., vol. 102, pp. 407-417, 2015.
A. Toktas and A. Akdagli, “Computation of resonant frequency of E-shaped compact microstrip antennas,” J. Fac. Eng. Arch. Gazi Univ., vol. 27, pp. 847-854, 2012.
S. Bhunia, “Effects of slot loading on microstrip patch antennas”, International journal of wired and wireless communications, vol. 1, pp. 1-6, 2012.
W. F. Richards, Y. T. Lo and D. D. Harrisson, “An improved theory for microstrip antennas and applications,” IEEE T. Anten. Propag., vol. 29, pp. 38-46, 1981.
K. Bhattacharyya and R. Garg, “A generalized transmission line model for microstrip patches,” IEE PROC-H., vol. 132, pp. 93-98, 1985.
D. B. Davidson, Computational electromagnetics for RF and microwave engineering, Cambridge University Press, Cambridge, United Kingdom.2005.
A. Akdagli, A. Toktas, M. B. Bicer, A. Kayabasi, D. Ustun and K. Kurt, “ANFIS model for determining resonant frequency of rectangular ring compact microstrip antennas,” Int. J. Appl. Electrom., vol. 46, pp. 483-490, 2014.
K. Guney and N. Sarikaya, “Adaptive neuro-fuzzy inference system for computing the resonant frequency of circular microstrip antennas,” The Applied Computational Electromagnetic Society, vol. 19, pp. 188-197, 2004.
F. Güneş, N. T. Tokan and F. Gürgen, “A consensual modeling of the expert systems applied to microwave devices,” International Journal of RF and Microwave computer-Aided Engineering, vol. 20, pp. 430-440, 2010.
Y. Tighilt, F. Bouttout and A. Khellaf, “Modeling and design of printed antennas using neural networks,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 21, pp. 228-233, 2011.
A. R. Venmathi and L. Vanitha, “Support vector machine for bandwidth analysis of slotted microstrip antenna,” International Journal of Computer Science, Systems Engineering and Information Technology, vol. 4, pp. 67-61, 2011.
K.Sabancı, M. Kokulu, “The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals” International Journal of Intelligent Systems and Applications in Engineering, 3(4), 127-130, 2015
M.F. Unlersen, K. Sabancı “The Classification of Diseased Trees by Using kNN and MLP Classification Models According to the Satellite Imagery” International Journal of Intelligent Systems and Applications in Engineering, 4(2), 25–28, 2016.
K.Sabanci, M. Akkaya, “Classification of Different Wheat Varieties by Using Data Mining Algorithms.” ” International Journal of Intelligent Systems and Applications in Engineering, 4(2), 40–44, 2016.
HyperLynx® 3D EM, Version 15, Mentor Graphics Corporation, 8005 SW Boeckman Road, Wilsonville, OR 97070.
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