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Yigit, Enes
Advanced Technology and Science (ATScience)
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Operating Frequency Estimation of Slot Antenna by Using Adapted kNN Algorithm Yigit, Enes
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018637927

Abstract

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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