Yazar "Uzer, M. S." seçeneğine göre listele

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    Bandwidth Modeling of U-slot Rectangular Microstrip Antennas with Artificial Neural Networks
    (ELECTROMAGNETICS ACAD, 2012) Uzer, M. S.; Uzer, D.; Yilmaz, N.; Gultekin, S. S.
    In this study, rectangular U-slot microstrip antennas are modeled by an Artificial Neural Network structure and their bandwidth values are calculated with the model. The antenna parameters are taken from in the literature. The experimental results are compared with Artificial Neural Network results. In comparison of the experimental and Artificial Neural Network results, accuracy rates of training and testing were found to be 96.6% and 91.3%, respectively. It is obtained that Artificial Neural Network results are compatible with experimental bandwidth results from the literature. It is shown that possible bandwidth behavior of U-slot rectangular microstrip patch antennas with this Artificial Neural Network model is powerfully estimated.
  • Küçük Resim Yok
    Öğe
    Effect of U-slot Applications on Circular Microstrip Patches Modeling with Artificial Neural Networks on Impedance Bandwidth
    (ELECTROMAGNETICS ACAD, 2012) Uzer, D.; Uzer, M. S.; Gultekin, S. S.; Yilmaz, N.
    In this study, effects of etching U-slot on circular patches on their bandwidths are investigated. The study is carried out by etching U-slots on the present microstrip patches in the literature by simulating them with HFSS. Simulation results are used for training and testing of an Artificial Neural Network model for different microstrip patches' bandwidth predictions. The obtained simulation results are compared with Artificial Neural Network results. In comparison of the simulation and Artificial Neural Network results, accuracy rates of training and testing were found as 93.28% and 94.19%, respectively. It is obtained that by etching U-slot, the bandwidths can be enhanced for the present circular microstrip patches. It is shown that possible the bandwidth behavior of U-slot circular microstrip disc antenna with this Artificial Neural Network model is powerfully estimated.