Intelligent Design Prediction of a Circular Polarized Antenna for CubeSat Application Using Machine Learning Algorithms

Intelligent Design Prediction of a Circular Polarized Antenna for CubeSat Application Using Machine Learning Algorithms

This paper presents an intelligent design method for a corner-truncated microstrip patch antenna (CTMPA) operating at 32 GHz using various well-known machine learning (ML) techniques. Our objectives are to obtain a gain of >5 dBic across a 10% bandwidth, an axial ratio (AR) of

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Veröffentlicht in:Electronics (Basel) 2023-10, Vol.12 (20), p.4195
Hauptverfasser: Uddin, Md Nazim, Islam, Md Khadimul, Ortiz, Michael, Alwan, Elias A.
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Antennas
Antennas (Electronics)
Artificial neural networks
Computation
Computer simulation
Cubesat
Data mining
Datasets
Decision trees
Design and construction
Design optimization
Design parameters
Machine learning
Mean square errors
Microwave wiring
Neural networks
Patch antennas
Regression analysis
Regression models
Regularization methods
Root-mean-square errors
Simulation
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Zusammenfassung:This paper presents an intelligent design method for a corner-truncated microstrip patch antenna (CTMPA) operating at 32 GHz using various well-known machine learning (ML) techniques. Our objectives are to obtain a gain of >5 dBic across a 10% bandwidth, an axial ratio (AR) of
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12204195