Ashrf Aoad
Department of Electrical and Electronics Engineering, Istanbul Sabahattin Zaim University, Turkey
Murat Simsek
Department of Astronautics Engineering, Istanbul Technical University, Turkey
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http://dx.doi.org/10.3384/ecp17142540Published in: Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Linköping Electronic Conference Proceedings 142:78, p. 540-543
Published: 2018-12-19
ISBN: 978-91-7685-399-3
ISSN: 1650-3686 (print), 1650-3740 (online)
This paper presents the use of inverse artificial neural networks (ANNs) to develop and optimize a reconfigurable 5-fingers shaped microstrip patch antenna. New solutions are produced by using three accurate prior knowledge inverse ANNs with sufficient amount of training data where the frequency information is incorporated into the structure of ANNs. The proposed antenna can operate with four modes, which are controlled by two PIN diode switches with ON/OFF states, and it resonates at multiple frequencies between 2-7 GHz. The complexity of the input/output relationship is reduced by using prior knowledge. Three independent 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.
artificial neural networks, reconfigurable microstrip antenna, prior knowledge input
