Efficient Neural Model of the Coplanar Inverted-F Antenna Based on MLP Networks

This research offers an EM model and a neural model based on two MultiLayer Perceptron (MLP) networks of a Coplanar Inverted F antenna. The input parameters of the neural model are ground plane length (l), height of antenna from the ground plane (h), the length of the open end (l o ) and the length...

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Bibliographic Details
Published in2023 IEEE 33rd International Conference on Microelectronics (MIEL) pp. 1 - 4
Main Authors Pesic, K., Gajic, M., Stankovic, Z., Doncov, N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.10.2023
Subjects
Analytical models
Mathematical models
Microelectronics
Multilayer perceptrons
Neural networks
Resonant frequency
Training
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Summary:This research offers an EM model and a neural model based on two MultiLayer Perceptron (MLP) networks of a Coplanar Inverted F antenna. The input parameters of the neural model are ground plane length (l), height of antenna from the ground plane (h), the length of the open end (l o ) and the length of the shorter end (l s ). The realized EM model is used to generate samples that are used for training and testing the neural model. The neural model is used to quickly find the resonant frequency in range [300-3000] MHz and minimum value of the S 11 parameter of a Coplanar Inverted F antenna.
ISSN:2159-1679
DOI:10.1109/MIEL58498.2023.10315843