Abstract
Microwave structures behavior prediction is an important research topic
in radio frequency (RF) design. In recent years, deep-learning-based
techniques have been widely implemented to study microwaves, and they
are envisaged to revolutionize this arduous and time-consuming work.
However, empirical data collection and neural network training are two
significant challenges of applying deep learning techniques to practical
RF modeling and design problems. To this end, this letter investigates a
transfer-learning-based approach to improve the accuracy and efficiency
of predicting microwave structure behaviors. Through experimental
comparisons, we validate that the proposed approach can reduce the
amount of data required for training while shortening the neural network
training time for the behavior prediction of microwave structures.