Artificial Neural Network Synthesis Based Approach For Superdirective Antenna Arrays Design

Abstract

In this work, a novel technique is proposed to design superdirective antenna arrays using artificial neural network (ANN). A radial basis function neural network model (RBNN) is developed and used to determine the optimal inter-elements separating distance and its corresponding excitation coefficients for a single frequency. The developed model is deployed for designing a three stacked S-shaped monopole antenna array. The simulation result show that a maximal directivity of 10 dBi for a separation distance of 0.16 λ, was achieved.