A Simple and Efficient Approach to Compute the Operating Frequency of Annular Ring Patch Antennas by Using ANN with Bayesian Regularization Learning Algorithm

Abstract

An annular ring patch antenna (ARPA) constructed by loading a circular slot in the center of the circular patch antenna is a popular microstrip antenna due to its favourable properties. 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 annular ring ARPAs in UHF band.  Firstly, the operating frequencies of 80 ARPAs having varied dimensions and electrical parameters were simulated with IE3DTM packaged software based on method of moment (MoM) in order to generate the data set for training and testing processes of the ANN model. Then ANN model was built with data set and while 70 simulated ARPAs and remaining 10 simulated ARPAs were employed for ANN model training and testing respectively. The proposed ANN model were confirmed by comparing with the suggestions reported elsewhere via measurement data published earlier in the literature. These results show that ANN model with BR learning algorithm can be successfully used to compute the operating frequency of ARPAs. 

Keywords

• Annular ring patch antenna,operating frequency,Artificial neural network,Bayesian regularization learning algorithm

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