Modeling Broadband Microwave Structures by Artificial Neural Networks
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Date
2004-06
Authors
Raida, Zbynek
Lukes, Zbynek
Otevrel, Viktor
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Společnost pro radioelektronické inženýrství
Abstract
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.
Description
Citation
Radioengineering. 2004, vol. 13, č. 2, s. 3-11. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2004/04_02_03_11.pdf
http://www.radioeng.cz/fulltexts/2004/04_02_03_11.pdf
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en