CAD of Stacked Patch Antennas Through Multipurpose Admittance Matrices From FEM and Neural Networks

Córcoles Ortega, Juan, González de Aza, Miguel Ángel ORCID: https://orcid.org/0000-0003-2156-2611 and Zapata Ferrer, Juan ORCID: https://orcid.org/0000-0003-3228-5621 (2008). CAD of Stacked Patch Antennas Through Multipurpose Admittance Matrices From FEM and Neural Networks. "Microwave and Optical Technology Letters", v. 50 (n. 9); pp. 2411-2416. ISSN 0895-2477. https://doi.org/10.1002/mop.23670.

Description

Title: CAD of Stacked Patch Antennas Through Multipurpose Admittance Matrices From FEM and Neural Networks
Author/s:
Item Type: Article
Título de Revista/Publicación: Microwave and Optical Technology Letters
Date: September 2008
ISSN: 0895-2477
Volume: 50
Subjects:
Freetext Keywords: computer-aided design, finite element methods, neural networks, optimization algorithms, stacked microstrip antennas
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Electromagnetismo y Teoría de Circuitos [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In this work, a novel computer-aided design methodology for probe-fed, cavity-backed, stacked microstrip patch antennas is proposed. The methodology incorporates the rigor of a numerical technique, such as finite element methods, which, in turn, makes use of a newly developed procedure (multipurpose admittance matrices) to carry out a full-wave analysis in a given structure in spite of certain physical shapes and dimensions not yet being established. With the aid of this technique, we form a training set for a neural network, whose output is the desired response of the antenna according to the value of design parameters. Last, taking advantage of this neural network, we perform a global optimization through a genetic algorithm or simulated annealing to obtain a final design. The proposed methodology is validated through a real design whose numerical results are compared with measurements with good agreement.

More information

Item ID: 2562
DC Identifier: https://oa.upm.es/2562/
OAI Identifier: oai:oa.upm.es:2562
DOI: 10.1002/mop.23670
Official URL: http://www3.interscience.wiley.com/journal/1198815...
Deposited by: Memoria Investigacion
Deposited on: 15 Apr 2010 10:47
Last Modified: 20 Apr 2016 12:13
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