Microstrip Antenna Design Using CST Optimized By Neural Network Algorithm

Authors

Hadeer. A Shoeab, Electronics and Communications Engineering Department, Mansoura University, Mansoura, EgyptFollow
Mohamed A.Mohamed, Electronics and Communications Engineering Department, Mansoura University, Mansoura, Egypt
Marzouk El Said A, Electronics and Communications Engineering Department, Mansoura University, Mansoura, Egypt
Ahmed. A. Kabeel, Electronics and Communications Engineering Department, Higher institute of engineering and Technology in New Damietta, New Damietta, Egypt

Corresponding Author

Hadeer. A Shoeab

Subject Area

Electronics and Communication Engineering

Article Type

Original Study

Abstract

In this paper, a general design procedure is suggested for the microstrip antennas using artificial neural networks and this is demonstrated using the rectangular patch geometry. The model was analyzed for 1733 data sets of input output parameters. 1300 samples for training and 433 samples for testing and 1500 epoch, learning rate from (0.003 to 0.005). Python was used to create and implement the ANN algorithm model. The mean error in detection of resonance frequencies (return loss peaks) was 0.144GHz on train set, and 0.116GHz on test set. The outputs of the radial basis function are optimized by varying the number of neurons and hidden layers. The proposed method's results are compared with the results of CST and found to be in good agreement.

Keywords

Return Loss, Computer Simulation Technology (CST), Microstrip Patch Antenna (MPA), Radial Basic Function (RBF), Artificial Neural Network (ANN)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Shoeab, Hadeer. A; A.Mohamed, Mohamed; El Said A, Marzouk; and Kabeel, Ahmed. A. (2023) "Microstrip Antenna Design Using CST Optimized By Neural Network Algorithm," Mansoura Engineering Journal: Vol. 48 : Iss. 3 , Article 4.
Available at: https://doi.org/10.58491/2735-4202.3045