Design of H-shaped MPA using Reptile Search Algorithm based Multilayer Perceptron Neural Network

Abstract

In many applications such as aerospace systems, satellites, mobile radar, and other wireless applications, the Microstrip patch antenna (MPA) plays an important role due to its properties like lightweight, low cost of production, and compact structure. Low gain, narrow frequency bandwidth, and high return loss are the shortcomings in these existing MPA design approaches. Moreover, the developed models of the antenna are hard to design and larger size. The antenna's geometrical specifications should be optimized to address this problem. This proposed approach Reptile Search Algorithm (RSA) based Multilayer perceptron (MLP) neural network is employed to design the H-shaped antenna for Ku-band applications. The MLP neural network is employed to calculate the fitness value of the RSA. To train the MLP neural network by using MATLAB software. The experimental and simulation results of the proposed approach shows better performance with 8.89 dB gain, -33.06 dB return loss, and 1.07 VSWR.