Hyperparameter Optimization of Long Short-Term Memory-Based Forecasting DNN for Antenna Modeling Through Stochastic Methods

This letter presents an impressive optimization method for determining the optimal model hyperparameters of a deep neural network (DNN) targeted to model the characteristics of antennas. In this letter, we propose an innovative approach of efficient yield analysis for modeling and sizing antennas. It is based on the long short-term memory DNN aiming to forecast the extended frequency responses, where various stochastic methods are applied for determining the optimal hyperparameters while training a DNN. Among the various methods, the one which models the antenna accurately in terms of input scattering parameter, gain, and radiation patterns is the winner. The proposed method is compact and addresses the problem of heavy reliance to the designer experience in determining the hyperparameters. Additionally, forecasting the future frequency responses of the antenna reduces the designer's effort substantially in measuring large frequency band; hence, measuring the whole frequency band would not be needed. For validating the effectiveness of the proposed method, the fabricated two element antenna array is used for modeling, where the results demonstrate that the Thompson sampling algorithm can determine optimal hyperparameters with minimum error in comparison with other reported stochastic methods leads to predict the future frequency band accurately.