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 |
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| creator | Uddin, Md Nazim Islam, Md Khadimul Ortiz, Michael Alwan, Elias A. |
| description | 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 |
| doi_str_mv | 10.3390/electronics12204195 |
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Our objectives are to obtain a gain of >5 dBic across a 10% bandwidth, an axial ratio (AR) of <3 dB, and a return loss of <−10 dB. First, a dataset of 715 full-wave simulated samples is analyzed with four distinct antenna characteristics (viz. features), along with the related computed |S11|, gain, and AR. Using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R2 score, 12 ML regression models were examined to compare the training data with the new predicted values. Next, the model that best satisfies our objectives was chosen. Results showed that the artificial neural network (ANN) followed by k-nearest neighbor (KNN) regression produced the lowest error compared to all tested ML models. The design parameters that achieved our intended objectives were computed using the predicted results. The predicted design was validated using a full-wave simulation and a prototype measurement.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics12204195</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>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</subject><ispartof>Electronics (Basel), 2023-10, Vol.12 (20), p.4195</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. 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| subjects | 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 |
| title | Intelligent Design Prediction of a Circular Polarized Antenna for CubeSat Application Using Machine Learning Algorithms |
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