BANDWIDTH ANALYSIS BY INTRODUCING SLOTS IN MICROSTRIP ANTENNA DESIGN USING ANN

Abstract

Many applications of microstrip antenna are rendered by their inherent narrow bandwidth. In this paper, a new approach is proposed to design inset feed microstrip antenna with slots in it to improve the antenna bandwidth. This paper deals with the design of slotted microsrip antenna on a substrate of thickness 1.588mm that gives wideband characteristics using ANN. The illustrated patch antenna gives enhanced bandwidth as compared to antenna with out slots of the same physical dimensions. In the present work an Artiflcial Neural Network (ANN) model is developed to analyse the bandwidth of the example antenna. The Method of Moments (MOM) based IE3D software has been used to generate training and test data for the ANN. The example antenna is also designed physically with glass epoxy substrate with r = 4:7 for few results for testing the artiflcial neural network model. The difierent variants of training algorithm of MLPFFB-ANN (Multilayer Perceptron feed forward back propagation Artiflcial Neural Network) and RBF-ANN (Radial basis function Artiflcial Neural Network) has been used to implement the network model. The results obtained from artiflcial neural network when compared with experimental and simulation results, found satisfactory and also it is concluded that RBF network is more accurate and fast as compared to difierent variants of backpropagation training algorithms of MLPFFBP.