An Application of ANN Model with Bayesian Regularization Learning Algorithm for Computing the Operating Frequency of C-Shaped Patch Antennas

A R T I C L E I N F O A B S T R A C T Article history: Received: 09 August, 2016 Accepted: 21 September, 2016 Online: 27 October, 2016 In this paper, an application of artificial neural network (ANN) using bayesian regularization (BR) learning algorithm based on multilayer perceptron (MLP) model is presented for computing the operating frequency of C-shaped patch antennas (CPAs) in UHF band. Firstly, the operating frequencies of 144 CPAs having varied dimensions and electrical parameters were simulated by the XFDTD software package based on the finitedifference time domain (FDTD) method in order to generate the data set for the training and testing processes of the ANN-BR model. Then ANN-BR model was built with data set and while 129 simulated CPAs and remaining 15 simulated CPAs were employed for ANNBR model training and testing respectively. In order to demonstrate its validity and accuracy, the proposed ANN-BR model was also tested over the simulation data given in the literature. The obtained results show that ANN-BR technique can be successfully used to compute the operating frequency of CPAs without involving any sophisticated methods.