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Analysis of Electromagnetic Band Gap Structure using Artificial Neural Network for UWB Applications

In this paper, a planar meander-line electromagnetic band-gap (EBG) structure for UWB applications is proposed. The EBG resonating at 7.5 GHz is designed and analyzed using artificial neural network (ANN) technique. The resonant frequency and the corresponding |S11| are predicted using geometrical parameters of the EBG unit cell. The proposed ANN model with single hidden layer is trained using Levenberg-Marquardt (LM) algorithm. The network is trained using different sets of hidden neurons in the hidden layer and activation function, in order to select the optimum ones. The least mean square error (MSE) is obtained when the network is trained using log-sigmoid transfer function with 40 hidden neurons in the hidden layer. Also, performance of the ANN model is validated using other statistical parameters viz. mean absolute percentage error (MAPE), and correlation coefficient (CC).