Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits

This chapter reviews the intersection of two major CAD technologies for modeling and design of RF and microwave circuits: artificial neural networks (ANNs) and space mapping (SM). A brief introduction to artificial neural networks is first pre-sented, starting from elementary concepts associated to...

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Bibliographic Details
Author: Rayas-Sánchez, José E.
Format: book part
Status:Published version
Publication Date:2013
Country:México
Institution:Instituto Tecnológico y de Estudios Superiores de Occidente
Repository:Repositorio Institucional del ITESO
Language:English
OAI Identifier:oai:rei.iteso.mx:11117/5939
Online Access:http://hdl.handle.net/11117/5939
Access Level:Open access
Keyword:Computer-aided Design (CAD)
Design Automation
RF and Microwave Modeling
EM-based Design Optimization
Artificial Neural Networks (ANN)
Space Mapping (SM)
Nowledge-based Neural Networks (KBNN)
Neural Space Mapping
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spelling Artificial neural networks and space mapping for EM-based modeling and design of microwave circuitsRayas-Sánchez, José E.Computer-aided Design (CAD)Design AutomationRF and Microwave ModelingEM-based Design OptimizationArtificial Neural Networks (ANN)Space Mapping (SM)Nowledge-based Neural Networks (KBNN)Neural Space MappingThis chapter reviews the intersection of two major CAD technologies for modeling and design of RF and microwave circuits: artificial neural networks (ANNs) and space mapping (SM). A brief introduction to artificial neural networks is first pre-sented, starting from elementary concepts associated to biological neurons. Elec-tromagnetics (EM)-based modeling and design optimization of microwave circuits using artificial neural networks is addressed. The conventional and most widely used neural network approach for RF and microwave design optimization is ex-plained, followed by brief descriptions of typical enhancing techniques, such as decomposition, design of experiments, clusterization and adaptive data sampling. More advanced approaches for ANN-based design exploiting microwave knowledge are briefly reviewed, including the hybrid EM-ANN approach, the pri-or-knowledge input method, and knowledge-based neural networks. Computa-tionally efficient neural space mapping methods for highly accurate EM-based design optimization are surveyed, contrasting different strategies for developing suitable (input and output) neural mappings. A high-level perspective is kept throughout the chapter, emphasizing the main ideas associated with these innova-tive techniques. A tutorial example using commercially available CAD tools is fi-nally presented to illustrate the efficiency of the neural space mapping methods.Springer2019-07-23T15:23:11Z2013-06info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionapplication/pdfJ. E. Rayas-Sánchez, “Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits,” in Surrogate-Based Modeling and Optimization: Applications in Engineering, S. Koziel and L. Leifsson, Ed., New York, NY: Springer, 2013, ch. 7, pp. 147-169.978-1-4614-7550-7http://hdl.handle.net/11117/5939reponame:Repositorio Institucional del ITESOinstname:Instituto Tecnológico y de Estudios Superiores de Occidenteinstacron:ITESOenghttp://quijote.biblio.iteso.mx/licencias/TodosLosDerechosReservados.pdfinfo:eu-repo/semantics/openAccessoai:rei.iteso.mx:11117/59392024-10-04T18:56:03Z
dc.title.none.fl_str_mv Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits
title Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits
spellingShingle Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits
Rayas-Sánchez, José E.
Computer-aided Design (CAD)
Design Automation
RF and Microwave Modeling
EM-based Design Optimization
Artificial Neural Networks (ANN)
Space Mapping (SM)
Nowledge-based Neural Networks (KBNN)
Neural Space Mapping
title_short Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits
title_full Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits
title_fullStr Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits
title_full_unstemmed Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits
title_sort Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits
dc.creator.none.fl_str_mv Rayas-Sánchez, José E.
author Rayas-Sánchez, José E.
author_facet Rayas-Sánchez, José E.
author_role author
dc.subject.none.fl_str_mv Computer-aided Design (CAD)
Design Automation
RF and Microwave Modeling
EM-based Design Optimization
Artificial Neural Networks (ANN)
Space Mapping (SM)
Nowledge-based Neural Networks (KBNN)
Neural Space Mapping
topic Computer-aided Design (CAD)
Design Automation
RF and Microwave Modeling
EM-based Design Optimization
Artificial Neural Networks (ANN)
Space Mapping (SM)
Nowledge-based Neural Networks (KBNN)
Neural Space Mapping
description This chapter reviews the intersection of two major CAD technologies for modeling and design of RF and microwave circuits: artificial neural networks (ANNs) and space mapping (SM). A brief introduction to artificial neural networks is first pre-sented, starting from elementary concepts associated to biological neurons. Elec-tromagnetics (EM)-based modeling and design optimization of microwave circuits using artificial neural networks is addressed. The conventional and most widely used neural network approach for RF and microwave design optimization is ex-plained, followed by brief descriptions of typical enhancing techniques, such as decomposition, design of experiments, clusterization and adaptive data sampling. More advanced approaches for ANN-based design exploiting microwave knowledge are briefly reviewed, including the hybrid EM-ANN approach, the pri-or-knowledge input method, and knowledge-based neural networks. Computa-tionally efficient neural space mapping methods for highly accurate EM-based design optimization are surveyed, contrasting different strategies for developing suitable (input and output) neural mappings. A high-level perspective is kept throughout the chapter, emphasizing the main ideas associated with these innova-tive techniques. A tutorial example using commercially available CAD tools is fi-nally presented to illustrate the efficiency of the neural space mapping methods.
publishDate 2013
dc.date.none.fl_str_mv 2013-06
2019-07-23T15:23:11Z
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv J. E. Rayas-Sánchez, “Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits,” in Surrogate-Based Modeling and Optimization: Applications in Engineering, S. Koziel and L. Leifsson, Ed., New York, NY: Springer, 2013, ch. 7, pp. 147-169.
978-1-4614-7550-7
http://hdl.handle.net/11117/5939
identifier_str_mv J. E. Rayas-Sánchez, “Artificial neural networks and space mapping for EM-based modeling and design of microwave circuits,” in Surrogate-Based Modeling and Optimization: Applications in Engineering, S. Koziel and L. Leifsson, Ed., New York, NY: Springer, 2013, ch. 7, pp. 147-169.
978-1-4614-7550-7
url http://hdl.handle.net/11117/5939
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv http://quijote.biblio.iteso.mx/licencias/TodosLosDerechosReservados.pdf
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://quijote.biblio.iteso.mx/licencias/TodosLosDerechosReservados.pdf
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositorio Institucional del ITESO
instname:Instituto Tecnológico y de Estudios Superiores de Occidente
instacron:ITESO
instname_str Instituto Tecnológico y de Estudios Superiores de Occidente
instacron_str ITESO
institution ITESO
reponame_str Repositorio Institucional del ITESO
collection Repositorio Institucional del ITESO
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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