preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202404.1158.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 16 April 2024 / Approved: 16 April 2024 / Online: 17 April 2024 (12:09:49 CEST)

The article presents an innovative method for synthesizing radiation patterns efficiently by 1 combining the Taguchi method and neural networks, while validating the results on a 10-element 2 antenna array. The Taguchi method aims to minimize product and process variability, while neural 3 networks are used to model the relationship between antenna design parameters and radiation 4 pattern characteristics. This approach utilizes Taguchi parameters as inputs for the neural network, 5 which is then trained on a dataset generated by the Taguchi method. After training, the network is 6 validated using a real 10-element antenna array. Analytical results demonstrate that this method 7 enables efficient synthesis of radiation patterns with a significant reduction in computation time 8 compared to traditional approaches. Furthermore, validation on the antenna array confirms the 9 accuracy and robustness of the approach, showing a high correlation between predicted performances 10 by the neural network model and actual measurements on the antenna array.In summary, our article 11 highlights that the combined use of the Taguchi method and neural networks, with validation on a 12 real antenna array, offers a promising approach for efficient synthesis of antenna radiation patterns. 13 This approach combines speed, accuracy, and reliability in antenna system design.

Taguchi; Neural Networks; Radiation pattern; Phased antenna array; Beamforming

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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