Comparison of Ensembles of Deep Neural Networks and Mixture of Experts for Antenna Modeling

Comparison of machine-learning antenna models, based on one deep neural network, an ensemble of neural networks and a mixture of experts, is presented. Two datasets comprising 100,000 samples each, with the sole difference in the ranges of antenna parameters, are used for training of considered machine learning approaches. Comparison in terms of accuracy and training time show a significant influence of the chosen design space (dataset) for training. One order of magnitude higher accuracy is achieved using a dataset generated with narrower ranges of antenna parameters. The newly proposed approach based on a mixture of experts achieved superior results when trained using wider ranges of parameters. If models are trained using narrower ranges of antenna parameters, then every model provides a unique compromise between the accuracy and training time.