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LAHIANI, M. RAIDA, Z. VESELÝ, J. OLIVOVÁ, J.
Original Title
Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks
Type
journal article in Web of Science
Language
English
Original Abstract
In this communication, artificial neural networks are used to estimate the initial structure of a multiband planar antenna. The neural networks are trained on a set of selected normalized multiband antennas characterized by time-efficient modal analysis with limited accuracy. Using the Deep Learning Toolbox in Matlab, several types of neural networks have been created and trained on the sample planar multiband antennas. In the neural network learning process, suitable network types were selected for the design of these antennas. The trained networks, depending on the desired operating bands, will select the appropriate antenna geometry. This is further optimized using Newton's method in HFSS. The use of the neural pre-design concept speeds up and simplifies the design of multiband planar antennas. The findings presented in this paper will be used to refine and accelerate the design of planar multiband antennas.
Keywords
multi-band antennas; feed-forward neural network; cascade-forward neural network; probabilistic neural network; full-wave analysis
Authors
LAHIANI, M.; RAIDA, Z.; VESELÝ, J.; OLIVOVÁ, J.
Released
12. 3. 2023
Publisher
MDPI
Location
BASEL
ISBN
2079-9292
Periodical
Electronics (MDPI)
Year of study
12
Number
6
State
Swiss Confederation
Pages from
1
Pages to
11
Pages count
URL
https://www.mdpi.com/2079-9292/12/6/1345
Full text in the Digital Library
http://hdl.handle.net/11012/213555
BibTex
@article{BUT183765, author="Mohamed Aziz {Lahiani} and Zbyněk {Raida} and Jiří {Veselý} and Jana {Olivová}", title="Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks", journal="Electronics (MDPI)", year="2023", volume="12", number="6", pages="11", doi="10.3390/electronics12061345", issn="2079-9292", url="https://www.mdpi.com/2079-9292/12/6/1345" }