Modeling frequency reconfigurable antenna array using neural networks

Amalendu Patnaik; Dimitrios Anagnostou; Christos G. Christodoulou; James C. Lyke

doi:10.1002/mop.20632

Modeling frequency reconfigurable antenna array using neural networks

Amalendu Patnaik^*, Dimitrios Anagnostou, Christos G. Christodoulou, James C. Lyke

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

In order to avoid the computational complexities involved in analyzing reconfigurable antennas, neural networks are used as an alternate approach. The neural network finds the location of the operational frequency hands for any combination of switches connecting different radiating elements. The network outputs are compared with the experimentally measured results.

Original language	English
Pages (from-to)	351-354
Number of pages	4
Journal	Microwave and Optical Technology Letters
Volume	44
Issue number	4
DOIs	https://doi.org/10.1002/mop.20632
Publication status	Published - 20 Feb 2005

Keywords

Antenna array
MEMS
Neural networks
Reconfigurable antennas

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Condensed Matter Physics
Electrical and Electronic Engineering

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10.1002/mop.20632

Cite this

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title = "Modeling frequency reconfigurable antenna array using neural networks",

abstract = "In order to avoid the computational complexities involved in analyzing reconfigurable antennas, neural networks are used as an alternate approach. The neural network finds the location of the operational frequency hands for any combination of switches connecting different radiating elements. The network outputs are compared with the experimentally measured results.",

keywords = "Antenna array, MEMS, Neural networks, Reconfigurable antennas",

author = "Amalendu Patnaik and Dimitrios Anagnostou and Christodoulou, {Christos G.} and Lyke, {James C.}",

year = "2005",

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AU - Patnaik, Amalendu

AU - Anagnostou, Dimitrios

AU - Christodoulou, Christos G.

AU - Lyke, James C.

PY - 2005/2/20

Y1 - 2005/2/20

N2 - In order to avoid the computational complexities involved in analyzing reconfigurable antennas, neural networks are used as an alternate approach. The neural network finds the location of the operational frequency hands for any combination of switches connecting different radiating elements. The network outputs are compared with the experimentally measured results.

AB - In order to avoid the computational complexities involved in analyzing reconfigurable antennas, neural networks are used as an alternate approach. The neural network finds the location of the operational frequency hands for any combination of switches connecting different radiating elements. The network outputs are compared with the experimentally measured results.

KW - Antenna array

KW - MEMS

KW - Neural networks

KW - Reconfigurable antennas

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U2 - 10.1002/mop.20632

DO - 10.1002/mop.20632

M3 - Article

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JO - Microwave and Optical Technology Letters

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Modeling frequency reconfigurable antenna array using neural networks

Abstract

Keywords

ASJC Scopus subject areas

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