Detail výsledku
Modeling broadband microwave structures by artificial neural networks
Lukeš Zbyněk, Ing., Ph.D., UREL (FEKT)
Otevřel Viktor, Ing., Ph.D., UREL (FEKT)
The paper describes the exploitation of feed-forward neural networks and recurrent neural networks for replacing full-wave numerical models of microwave structures in complex microwave design tools. Building a neural model, attention is turned to the modeling accuracy and to the efficiency of building a model. Dealing with the accuracy, we describe a method of increasing it by successive completing a training set.
Neural models are mutually compared in order to highlight their advantages and disadvantages. As a reference model for comparisons, approximations based on standard cubic splines are used.
Neural models are used to replace both the time-domain numeric models and the frequency-domain ones.
Artificial neural networks, frequency-domain finite elements, time-domain method of moments, wire antennas, microwave transmission lines.
@article{BUT42090,
author="Zbyněk {Raida} and Zbyněk {Lukeš} and Viktor {Otevřel}",
title="Modeling broadband microwave structures by artificial neural networks",
journal="Radioengineering",
year="2004",
volume="13",
number="2",
pages="9",
issn="1210-2512"
}