2x2 Array Microstrip Patch Antenna Designing and Validation with Artificial Neural Networks

Abstract

A novel and successful method for RF, microwave modelling, and antenna design has gained attention: computational modules based on neural networks. The behavior of active or passive components or circuits can be taught to neural networks. The design of a microstrip patch antenna utilizing an artificial neural network (ANN) is discussed in this research. Computer Simulation Technology (CST) is used to establish the antenna's size and characteristics in order to create a successful ANN model. The feed-forward back-propagation neural network and Levenberg Marquardt optimization algorithm are used to model the antenna design. High-Frequency Structure Simulator (HFSS) software, operating at 2.4 GHz, is used to build the initial microstrip patch (ISM band).A 2.4GHz operating frequency is used to create a 2*2 microstrip planar array. A variety of neural networks are evaluated and trained to produce the most ideal results after generating and modelling discoveries using HFSS simulation software and the Finite Difference Time Domain (FTDT) approach. In this research, neural networks are utilized. In order to identify the most ideal solution, optimization is performed using a radial basis function neural network (RBF NN) and a feed-forward backpropagation approach.