Deep neural network–based soft computing the resonant frequency of E–shaped patch antennas

Abstract

In this study, deep neural network (DNN) is implemented to soft computation of the resonant frequency of E–shaped patch antenna (ESPA). A DNN model is built on a 5-layer framework using an adaptive learning rate algorithm though a simulated database of 144 ESPAs. The framework and hyperparameters of the DNN model are optimized by exploiting K–fold cross validation method in the training phase. In order to generate the database for training and testing the model, 144 ESPAs with different geometrical and electrical parameters are simulated in terms of the resonant frequency using a precise electromagnetic analysis platform. The DNN model is trained on the dataset#130 with an average percentage error (APE) of 0.269 between the simulated and computed frequency values. For corroboration of the proposed DNN model is tested and verified by comparing with previously applied traditional neural networks on the remainder dataset#14, and even verified on a fabricated ESPA operating at 2.4 GHz. The DNN model computes the resonant frequency of the testing dataset#14 with the highest accuracy of 0.285 APE. The results show that the proposed deep neuro-computing model for the resonant frequency is a powerful and helpful technique as an easy alternative to prohibitive measurement and simulation.