Consensus Deep Neural Networks for Antenna Design and Optimization

Abstract

We present a general approach for antenna design and optimization based on consensus of results from a number of independently trained deep neural networks (DNNs). The aim of using the consensus is to reduce the uncertainty of results from a single DNN. The approach leads to several orders of magnitude faster antenna optimization and design compared to the optimization based on a full-wave solver and allows a compromise between the analysis speed and its accuracy. The used DNNs are multilayer perceptrons (MLP) with multiple fully connected hidden layers. As an example, we consider the Yagi–Uda antenna with four design parameters and optimize it for the maximal forward gain. The training of neural networks is done on datasets of several sizes, up to 1 million antenna samples. The samples are generated either randomly or at a uniform grid over the design space using the method of moments.