Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 5Development of Knowledge Based Response Correction for a Reconfigurable N-Shaped Microstrip Antenna Design(Institute of Electrical and Electronics Engineers Inc., 2015-08) Aoad, Ashrf; Simsek, Murat; Aydin, ZaferThis study presents the use of prior knowledge of inverse artificial neural network (ANN) to model and optimize a reconfigurable N-shaped microstrip antenna. Three accurate prior knowledge inverse ANNs with large amount training data are proposed where the frequency information is incorporated into the structure of ANN. The complexity of the input/output relationship is reduced by using prior knowledge. Three separate methods of incorporating knowledge in the second step of the training process with a multilayer perceptron (MLP) in the first step are demonstrated and their results are compared to EM simulation. © 2023 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 2Citation - Scopus: 5Design of a Tri Band 5-Fingers Shaped Microstrip Patch Antenna With an Adjustable Resistor(Institute of Electrical and Electronics Engineers Inc., 2014-11) Aoad, Ashrf; Aydin, Zafer; Korkmaz, ErdalThis paper presents a tri band 5-fingers shaped microstrip patch antenna, which resonates initially at dual band of 3.2 GHz and 5.2 GHz frequencies for VSWR < 2. The antenna is modified by adding an adjustable resistor between the conductor and the reflecting plane giving a third resonant frequency of 2.4 GHz. A decrease in the return loss at 2.4 GHz is observed by modifying the value of the resistance. Impedance bandwidth and the resonant frequencies are examined with respect to the variability of the parameters of the antenna and the position of the adjustable resistor. The size of the antenna has been reduced by 57.9% in length and 14.06% in width. The proposed antenna can be used for 4G, WLAN, and Wi-MAX. The antenna is designed and optimized by using the commercial CST software. © 2016 Elsevier B.V., All rights reserved.